inst/shiny/server.R
In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
#1GB max upload size
options(shiny.maxRequestSize = 1000 * 1024 ^ 2)
options(useFancyQuotes = FALSE)
options(shiny.autoreload = TRUE)
seurat.version <- packageVersion(pkg = "SeuratObject")
internetConnection <- suppressWarnings(Biobase::testBioCConnection())
source("partials.R", local = TRUE) # creates several smaller UI components
# R.utils::sourceDirectory("qc_help_pages")
source("qc_help_pages/ui_decontX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_soupX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_bcds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_bcds_hybrid_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_doubletFinder_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scrublet_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_dc_and_qcm_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scDblFinder_help.R", local = TRUE) # creates several smaller UI components
# source("server_partials/server_01_data.R", local = TRUE) # functions for Data section
# Define server logic required to draw a histogram
shinyServer(function(input, output, session) {
# PushBar setup
# setup_pushbar(blur = FALSE, overlay = FALSE)
# library(fs)
# library(shinyFiles)
#-----------------------------------------------------------------------------
# MISC - Used throughout app
#-----------------------------------------------------------------------------
#reactive values object
vals <- reactiveValues(
counts = getShinyOption("inputSCEset"),
original = getShinyOption("inputSCEset"),
batchRes = NULL,
gsvaRes = NULL,
gsvaLimma = NULL,
vamRes = NULL,
vamCdf = NULL,
vamResults = NULL,
vamScore = NULL,
gsvaScore = NULL,
gsvaResults = NULL,
visplotobject = NULL,
enrichRes = NULL,
celdaMod = NULL,
celdaList = NULL,
celdaListAll = NULL,
celdaListAllNames = NULL,
celdatSNE = NULL,
celdaModuleFeature = NULL,
dimRedPlot = NULL,
dimRedPlot_geneExp = NULL,
dendrogram = NULL,
pcX = NULL,
pcY = NULL,
showAssayDetails = FALSE,
hmCSPresets = list("RWB" = c("blue", "white", "red"),
"RdBu_r" = c("#2971b1", "#f7f6f6", "#b92732"),
"BrBG" = c("#995d12", "#f4f4f4", "#0c7068"),
"Blues" = c("#dae8f5", "#6daed4", "#0b559f"),
"Greens" = c("#E1F3F6", "#69C2A1", "#04702F")),
hmCSURL = NULL,
hmTmpColData = NULL,
hmTmpRowData = NULL,
hvgCalculated = list(status = FALSE, method = NULL),
fmHMshowHide = FALSE
)
#Update all of the columns that depend on pvals columns
updateColDataNames <- function(){
pdataOptions <- colnames(colData(vals$counts))
updateSelectInput(session, "soupXCluster", choices = c("None", pdataOptions))
updateSelectInput(session, "qcSampleSelect", choices = pdataOptions)
updateSelectInput(session, "filteredSample",
choices = c("none", pdataOptions))
updateSelectInput(session, "deleterowdatacolumn",
choices = pdataOptions)
updateSelectInput(session, "colorBy",
choices = c("No Color", "Gene Expression", pdataOptions))
updateSelectInput(session, "shapeBy",
choices = c("No Shape", pdataOptions))
updateSelectInput(session, "scMergeCT",
choices = c(pdataOptions))
updateSelectInput(session, "combatCond",
choices = pdataOptions)
updateSelectInput(session, "combatBioCond",
choices = c("None", pdataOptions))
updateSelectInput(session, "batchCorrVar",
choices = pdataOptions)
updateSelectInput(session, "batchCheckVar",
choices = pdataOptions)
updateSelectInput(session, "batchCheckCond",
choices = c("None", pdataOptions))
updateSelectInput(session, "clustVisCol", choices = pdataOptions)
updateSelectInput(session, "deC1Class",
choices = pdataOptions)
updateSelectInput(session, "deC2G1Col",
choices = pdataOptions)
updateSelectInput(session, "deC2G2Col",
choices = pdataOptions)
updateSelectInput(session, 'deCovar', choices = pdataOptions)
updateSelectInput(session, "deHMcolData",
choices = pdataOptions)
updateSelectInput(session, "deHMSplitCol",
choices = c('condition', pdataOptions),
selected = 'condition')
updateSelectInput(session, "fmCluster", choices = pdataOptions)
updateSelectInput(session, "fmHMcolData",
choices = pdataOptions)
updateSelectInput(session, "hmCellAnn", choices = pdataOptions)
updateSelectInput(session, "pathwayPlotVar",
choices = pdataOptions)
updateSelectInput(session, "selectReadDepthCondition",
choices = pdataOptions)
updateSelectInput(session, "selectCellNumCondition",
choices = pdataOptions)
updateSelectInput(session, "selectSnapshotCondition",
choices = pdataOptions)
updateSelectInput(session, "annotModifyChoice",
choices = c("none", pdataOptions))
updateSelectInput(session, "hmCellCol",
choices = pdataOptions)
updateSelectInput(session, "hmCellTextBy",
choices = c("Row Names", pdataOptions))
updateSelectInput(session, 'hmAddCellLabel',
choices = c("Default cell IDs", pdataOptions))
updateSelectInput(session, "ctLabelByCluster",
choices = pdataOptions)
if (!is.null(hmTemp$sce)) {
hmAnnAllColors$col <- dataAnnotationColor(hmTemp$sce, 'col')
}
updateSelectInput(session, "TSCANclusterName",
choices = c("Auto generate clusters", pdataOptions))
}
updateGeneNames <- function(){
selectthegenes <- rownames(vals$counts)
updateSelectizeInput(session, "colorGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "selectvisGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "enrichGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "plotTSCANDimReduceFeatures_features",
choices = selectthegenes, server = TRUE)
}
updateFeatureAnnots <- function(){
selectRowData <- colnames(rowData(vals$counts))
my_list <- data.frame()
for(i in selectRowData) {
my_list[i,1] <- paste0(i, " (e.g. ", paste(head(rowData(vals$counts)[,i], n = 3), collapse = ","), ")")
}
selectRowDataWithExamples <- as.character(my_list[,1])
selectNonNARowData <- names(apply(rowData(vals$counts), 2, anyNA)[apply(rowData(vals$counts), 2, anyNA) == FALSE])
my_list2 <- data.frame()
for(j in selectNonNARowData) {
my_list2[j,1] <- paste0(j, " (e.g. ", paste(head(rowData(vals$counts)[,j], n = 3), collapse = ","), ")")
}
selectNonNARowDataWithExamples <- as.character(my_list2[,1])
Default <- paste0("Default (e.g. ", paste(head(rownames(vals$counts), n = 3), collapse = ","), ")")
updateSelectInput(session, "gsByParam",
choices = c("rownames", selectRowData))
updateSelectInput(session, "importFeatureDispOpt",
choices = c(Default,
selectRowDataWithExamples))
updateSelectInput(session, "importFeatureNamesOpt",
choices = c(Default,
selectNonNARowDataWithExamples))
updateSelectInput(session, "filteredFeature",
choices = c("none", selectRowData))
updateSelectInput(session, "hvgPlotFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "fmHMFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "deHMrowData",
choices = selectRowData)
updateSelectInput(session, "deHMSplitRow",
choices = c('regulation', selectRowData),
selected = 'regulation')
updateSelectInput(session, "deHMrowLabel",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "deVolcFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'deVioLabel',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'deRegLabel',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'tscanDEFeatureDisplay',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'plotTSCANClusterDEG_featureDisplay',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "fmHMrowData",
choices = selectRowData)
updateSelectInput(session, "hmGeneCol",
choices = selectRowData)
updateSelectInput(session, "hmGeneTextBy",
choices = c("Row Names", selectRowData))
updateSelectInput(session, 'hmGeneAnn', choices = selectRowData)
updateSelectInput(session, 'hmAddGeneLabel',
choices = c("Default feature IDs", selectRowData))
if (!is.null(hmTemp$sce)) {
hmAnnAllColors$row <- dataAnnotationColor(hmTemp$sce, 'row')
}
}
updateNumSamples <- function(){
numsamples <- ncol(vals$counts)
updateNumericInput(session, "downsampleNum", value = numsamples,
max = numsamples)
}
updateSelectInputTag <- function(session, inputId, choices = NULL,
selected = NULL, label = "Select assay:",
tags = NULL, recommended = NULL,
showTags = TRUE, redDims = FALSE,
inSCE = vals$counts){
choices <- expTaggedData(inSCE, tags, redDims = redDims, showTags = showTags, recommended = recommended)
updateSelectizeInput(session = session, inputId = inputId, label = label, choices = choices, selected = selected)
}
updateAssayInputs <- function(){
currassays <- names(assays(vals$counts))
updateSelectInputTag(session, "dimRedAssaySelect",
label = "Select Input Matrix:",
recommended = c("transformed", "hvg"),
choices = expDataNames(vals$counts))
updateSelectInputTag(session, "dimRedAssaySelect_tsneUmap",
label = "Select Input Matrix:",
recommended = c("redDims"),
redDims = TRUE)
updateSelectInputTag(session, "batchCheckAssay", choices = currassays)
updateSelectInputTag(session, "batchCheckOrigAssay", choices = currassays)
updateSelectInputTag(session, "clustScranSNNMat",
label = "Select Input Matrix:",
choices = expDataNames(vals$counts),
recommended = "redDims", redDims = TRUE)
if (is.null(input$deMethod)) {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed"), redDims = TRUE)
} else if (input$deMethod == "DESeq2") {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "uncategorized"),
recommended = c("raw"))
} else {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed"), redDims = TRUE)
}
if (is.null(input$fmMethod)) {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
} else if (input$fmMethod == "DESeq2") {
updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
} else {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
}
updateSelectInputTag(session, "fmHMAssay", choices = currassays,
selected = input$fmAssay)
updateSelectInputTag(session, "pathwayAssay",
recommended = c("transformed", "normalized", "scaled"))
updateSelectInputTag(session, "vamAssay",
recommended = c("transformed", "normalized", "scaled"))
updateSelectInputTag(session, "modifyAssaySelect")
updateSelectInputTag(session, "normalizeAssaySelect",
label = "Select assay to normalize:",
recommended = "raw")
updateSelectInputTag(session, "seuratSelectNormalizationAssay",
choices = currassays, showTags = FALSE)
updateSelectInputTag(session, "scanpySelectNormalizationAssay",
choices = currassays, showTags = FALSE)
if(input$hvgMethodFS == "vst"){
updateSelectInputTag(session, "assaySelectFS_Norm", recommended = c("raw"))
}
else{
updateSelectInputTag(session, "assaySelectFS_Norm",
recommended = c("transformed", "normalized"))
}
updateSelectInputTag(session, "filterAssaySelect", choices = currassays)
updateSelectInputTag(session, "qcAssaySelect", recommended = "raw", inSCE = vals$original)
updateSelectInputTag(session, "celdaAssay", choices = currassays)
updateSelectInputTag(session, "celdaAssayGS", choices = currassays)
updateSelectInputTag(session, "celdaAssaytSNE", choices = currassays)
updateSelectInputTag(session, "celdaAssayProbabilityMap",
choices = currassays)
updateSelectInputTag(session, "celdaAssayModuleHeatmap",
choices = currassays)
updateSelectInputTag(session, "depthAssay", choices = currassays)
updateSelectInputTag(session, "cellsAssay", choices = currassays)
updateSelectInputTag(session, "snapshotAssay", choices = currassays)
updateSelectInputTag(session, "exportAssay", choices = currassays)
updateSelectInputTag(session, "hmAssay", recommended = "transformed")
updateSelectInputTag(session, "bpAssay", recommended = "transformed")
updateSelectInputTag(session, "ctLabelAssay", choices = currassays,
recommended = c("transformed"))
# batch correction assay conditions
bc.recommended <- NULL
method.log <- c("FastMNN", "Limma", "MNN")
method.scale <- c("BBKNN")
method.raw <- c("ZINBWaVE", "ComBatSeq")
if (is.null(input$batchCorrMethods)) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.log) {
bc.recommended <- c("transformed")
} else if (input$batchCorrMethods %in% method.raw) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.scale) {
bc.recommended <- "scaled"
}
updateSelectInputTag(session, "batchCorrAssay",
label = "Select Assay to Correct:",
choices = currassays,
recommended = bc.recommended)
updateSelectInputTag(session, "AdvancedMethodSelect_Colorby",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "AdvancedMethodSelect_Xaxis",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "AdvancedMethodSelect_Yaxis",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "TSCANassayselect", choices = currassays,
recommended = "transformed")
updateSelectInputTag(session, "TSCANBranchAssaySelect",
choices = currassays,
recommended = "transformed")
updateSelectInputTag(session, "plotTSCANDimReduceFeatures_useAssay",
choices = currassays,
recommended = "transformed")
}
updateGeneSetSelection <- function() {
allGS <- sctkListGeneSetCollections(vals$counts)
if (length(allGS) == 0) {
updateSelectizeInput(session, "PathwayGeneLists",
choices = "Import geneset before using")
} else {
updateSelectizeInput(session, "PathwayGeneLists", choices = allGS)
}
updateSelectInput(session, "gsExisting", choices = c("None", allGS))
names(allGS) <- allGS
updateSelectInput(session, "QCMgeneSets", choices =c("None", allGS))
}
observeEvent(vals$original, {
if (!is.null(vals$original)) {
#if (!is.null(metadata(vals$original)$sctk$genesets)) {
#newGSchoices <- sctkListGeneSetCollections(vals$original)
#updateSelectInput(session, "gsExisting", choices = c("None", newGSchoices))
#updateSelectInput(session, "QCMgeneSets", choices =c("None", newGSchoices))
#shinyjs::show(id = "gsAddToExisting", anim = FALSE)
#} else {
#shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
#updateSelectInput(session, "gsExisting", choices = c("None"), selected = "None")
#updateSelectInput(session, "QCMgeneSets", choices =c("None"), selected = "None")
#}
shinyjs::show(id="combineOptions")
#gsByChoices <- c("None", "rownames", names(rowData(vals$original)))
#updateSelectInput(session, "gsByParam", choices = gsByChoices, selected = "rownames")
} else {
shinyjs::hide(id="combineOptions")
}
})
updateReddimInputs <- function(){
currreddim <- names(reducedDims(vals$counts))
updateSelectInput(session, "FastMNNReddim", choices = currreddim)
updateSelectInput(session, "HarmonyReddim", choices = currreddim)
updateSelectInput(session, "selectRedDimPlot_tsneUmap",
choices = currreddim)
updateSelectInput(session, "clustVisReddim", choices = currreddim)
updateSelectInput(session, "clustKMeansReddim", choices = currreddim)
updateSelectInput(session, "clustSeuratReddim", choices = currreddim)
updateSelectInput(session, "QuickAccess",
choices = c(currreddim, "Custom"))
updateSelectInput(session, "ApproachSelect_Xaxis", choices = currreddim)
updateSelectInput(session, "ApproachSelect_Yaxis", choices = currreddim)
updateSelectInput(session, "ApproachSelect_Colorby", choices = currreddim)
suppressWarnings({availPathwayRes <- getPathwayResultNames(vals$counts)})
updateSelectizeInput(session, "pathwayRedDimNames",
choices = availPathwayRes)
updateSelectInput(session, "TSCANReddim", choices = currreddim)
updateSelectInput(session, "TSCANVisRedDim", choices = currreddim)
updateSelectInput(session, "DEClusterRedDimNames", choices = currreddim)
updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim", choices = currreddim)
updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim", choices = currreddim)
}
observeEvent(input$consoleToggle, {
shinyjs::toggle(id = "consolePanel")
})
# Stop auto-scroll console tab
observeEvent(input$logDataAutoScrollStatus, {
stopAutoScroll <- paste0("clearInterval(", input$logDataAutoScrollStatus, ");")
shinyjs::runjs(stopAutoScroll)
})
# js$disableTabs()
# Close app on quit
# session$onSessionEnded(stopApp)
#-----------------------------------------------------------------------------
# Page 1: Upload ####
#-----------------------------------------------------------------------------
sysname <- Sys.info()[['sysname']]
if (sysname == "Windows") {
roots <- getVolumes()()
} else {
roots <- c(home = "~/")
}
dirPaths <- reactiveValues(
bDirectory = ".",
sDirectory = ".",
directory = ".",
outputDirectory = "."
)
# Upload data through shiny app
allImportEntries <- reactiveValues(samples=list(), id_count=0)
shinyDirChoose(input, "bDirectory", roots = roots)
shinyDirChoose(input, "sDirectory", roots = roots)
shinyDirChoose(input, 'directory', roots = roots)
output$bDirectoryPath <- renderText({
dirPaths$bDirectory
})
output$sDirectoryPath <- renderText({
dirPaths$sDirectory
})
output$directoryPath <- renderText({
dirPaths$directory
})
# event listener for the base directory modal (need to populate table for sample names)
# see https://github.com/wleepang/shiny-directory-input
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$bDirectory
},
handlerExpr = {
if ("path" %in% names(input$bDirectory)) {
# condition prevents handler execution on initial app launch
#path = choose.dir(default = readDirectoryInput(session, 'bDirectory'),
# caption="Choose a directory")
#updateDirectoryInput(session, 'bDirectory', value = path)
vol <- roots[[input$bDirectory$root]]
dirPaths$bDirectory <- paste0(vol, paste(unlist(input$bDirectory$path[-1]),
collapse = .Platform$file.sep))
path <- dirPaths$bDirectory
# clear the previous table of sample names
prevPath <- path
count <- 0
for (prev in list.dirs(prevPath, recursive = FALSE)) {
count <- count+1
removeUI(
selector = paste0("#sampleRow", count)
)
}
# create a new table for the selected directory
count <- 0
if (!is.na(path)) {
# Add Reference selection for cellRangerV2
if (input$uploadChoice == "cellRanger2") {
## Identify available reference
firstSampleDir <- list.dirs(path, recursive = FALSE)[1]
refPath <- file.path(firstSampleDir, "outs/filtered_gene_bc_matrices")
refList <- basename(list.dirs(refPath, recursive = FALSE))
## Add UI
insertUI(
selector = "#bDirTable",
ui = fluidRow(
column(
6,
selectInput("cr2_b_Ref", "Reference:", refList)
)
)
)
}
# Add Sample Rename rows
counts <- vector()
for (sample in list.dirs(path, recursive = FALSE)) {
count <- count+1
counts <- c(counts, count)
insertUI(
selector = "#bDirTable",
ui = fluidRow(
id = paste0("sampleRow", count),
column(6, basename(sample)),
column(6, textAreaInput(paste0("sampleName", count), "Sample Name", resize = "none", value = basename(sample)))
)
)
}
}
}
}
)
# for sample directory modal
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$sDirectory
},
handlerExpr = {
#if (input$sDirectory > 0) {
# # condition prevents handler execution on initial app launch
# path = choose.dir(default = readDirectoryInput(session, 'sDirectory'),
# caption="Choose a directory")
# updateDirectoryInput(session, 'sDirectory', value = path)
# if (!is.na(path)) {
# updateTextInput(session, "sSampleID", value = basename(path))
# }
#}
if ("path" %in% names(input$sDirectory)) {
vol <- roots[[input$sDirectory$root]]
dirPaths$sDirectory <- paste0(vol, paste(unlist(input$sDirectory$path[-1]),
collapse = .Platform$file.sep))
path <- dirPaths$sDirectory
if (!is.na(path)) {
if (input$uploadChoice == "cellRanger2") {
## Identify available reference
refPath <- file.path(path, "outs/filtered_gene_bc_matrices")
refList <- basename(list.dirs(refPath, recursive = FALSE))
## Add UI
insertUI(
selector = "#sDirTable",
ui = fluidRow(
column(
6,
selectInput("cr2_s_Ref", "Reference:", refList)
)
)
)
}
updateTextInput(session, "sSampleID", value = basename(path))
}
}
}
)
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$directory
},
handlerExpr = {
#if (input$directory > 0) {
# # condition prevents handler execution on initial app launch
# path = choose.dir(default = readDirectoryInput(session, 'directory'),
# caption="Choose a directory")
# updateDirectoryInput(session, 'directory', value = path)
#}
if ("path" %in% names(input$directory)) {
vol <- roots[[input$directory$root]]
dirPaths$directory <- paste0(vol, paste(unlist(input$directory$path[-1]),
collapse = .Platform$file.sep))
}
}
)
# event listeners for "Add Sample" buttons
observeEvent(input$addCR2Sample, {
showModal(importCRModal())
})
observeEvent(input$crOpt1, {
removeModal()
showModal(importCRBDir())
})
observeEvent(input$crOpt2, {
removeModal()
showModal(importCRSDir())
})
observeEvent(input$crOpt3, {
removeModal()
showModal(importCRDDir())
})
observeEvent(input$addCR3Sample, {
showModal(importCRModal())
})
observeEvent(input$addSSSample, {
showModal(importStarModal())
})
observeEvent(input$addBUSSample, {
showModal(importModal())
})
observeEvent(input$addSEQSample, {
showModal(importModal(needsDir = TRUE))
})
observeEvent(input$addOptSample, {
showModal(importModal())
})
# event listener for "Remove Sample" buttons
observeEvent(input$clearAllImport, {
for (entry in allImportEntries$samples) {
removeUI(selector = paste0("#", entry$id))
}
allImportEntries$samples <- list()
})
# base directory
observeEvent(input$BDirOK, {
basePath <- dirPaths$bDirectory
# if the user doesn't specify a base directory, show the modal again with the warning message
if (identical(basePath, character(0))) {
showModal(importCRBDir(failed = TRUE))
} else {
allDirs <- list.dirs(basePath, recursive = FALSE)
# if we are adding a new CellRangerV2 sample
if (input$uploadChoice == "cellRanger2") {
allUI <- vector()
allIDs <- vector()
count <- 0
for (sample in allDirs) {
count <- count + 1
name <- input[[paste0("sampleName", count)]]
if (!nzchar(name)) {
name <- basename(sample)
}
id <- paste0("bnewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(cellRangerDirs = basePath,
sampleDirs = basename(sample),
sampleNames = name,
reference = input$cr2_b_Ref))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
ui_i <- fluidRow(
id = id,
tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
column(3, basePath),
column(3, basename(sample)),
column(3, name),
column(3, actionButton(paste0("remove", id), "X"))
)
allImportEntries$id_count <- allImportEntries$id_count + 1
allUI <- c(allUI, list(ui_i))
allIDs <- c(allIDs, id)
}
} else { # if we are adding a new CellRangerV3 sample
allUI <- vector()
allIDs <- vector()
count <- 0
for (sample in allDirs) {
count <- count + 1
name <- input[[paste0("sampleName", count)]]
if (!nzchar(name)) {
name <- basename(sample)
}
id <- paste0("bnewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = basePath, sampleDirs = basename(sample), sampleNames = name))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
ui_i <- fluidRow(
id = id,
tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
column(3, basePath),
column(3, basename(sample)),
column(3, name),
column(3, actionButton(paste0("remove", id), "X"))
)
allImportEntries$id_count <- allImportEntries$id_count + 1
allUI <- c(allUI, list(ui_i))
allIDs <- c(allIDs, id)
}
}
# insert all the new sample rows
for (i in seq_along(allUI)) {
insertUI(
selector = "#newSampleImport",
ui = allUI[i]
)
}
# create event handlers for all the remove buttons
# from: https://stackoverflow.com/questions/40038749/r-shiny-how-to-write-loop-for-observeevent
lapply(
X = allIDs,
FUN = function(id_i){
observeEvent(input[[paste0("remove", id_i)]], {
removeUI(
selector = paste0("#", id_i)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id_i) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
}
)
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event listeners for Cell Ranger import modals' OK buttons
# sample directory
observeEvent(input$SDirOK, {
samplePath <- dirPaths$sDirectory
# make sure a directory is selected
if (identical(samplePath, character(0))) {
showModal(importCRSDir(failed = TRUE))
} else {
# add the files to the appropriate reactiveValues
if (input$uploadChoice == "cellRanger2") {
id <- paste0("snewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(cellRangerDirs = dirname(samplePath),
sampleDirs = basename(samplePath),
sampleNames = input$sSampleID,
reference = input$cr2_s_Ref))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
} else {
id <- paste0("snewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = paste0(dirname(samplePath), "/"), sampleDirs = basename(samplePath), sampleNames = input$sSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
}
# add new row to table
addToGeneralSampleTable(input$uploadChoice, id, samplePath, input$sSampleID)
# handler to remove the sample that was just added
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# data directory
observeEvent(input$DDirOK, {
dataPath <- dirPaths$directory
if ((!nzchar(input$dSampleID)) || (identical(dataPath, character(0)))) {
showModal(importCRDDir(failed = TRUE))
} else {
if (input$uploadChoice == "cellRanger2") {
id <- paste0("dnewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(dataDir = dataPath,
sampleName = input$dSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
} else {
id <- paste0("dnewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(dataDir = dataPath, sampleName = input$dSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
}
# add new row to table
addToGeneralSampleTable(input$uploadChoice, id, dataPath, input$dSampleID)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event handler for pressing OK on the import Star modal
observeEvent(input$modalStarOk, {
basePath <- dirPaths$directory
curFiles <- list()
if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
showModal(importStarModal(failed = TRUE))
} else {
entry <- list()
id <- paste0("newSampleSS", allImportEntries$id_count)
entry <- list(type="starSolo", id = id, params=list(STARsoloDirs = basePath, samples = input$sampleName, STARsoloOuts = input$geneFolder))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event handler for pressing OK on the import modal
observeEvent(input$modalOk, {
basePath <- dirPaths$directory
curFiles <- list()
if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
showModal(importModal(failed = TRUE))
} else {
entry <- list()
if (input$uploadChoice == "busTools") {
id <- paste0("newSampleBUS", allImportEntries$id_count)
entry <- list(type="busTools", id = id, params=list(BUStoolsDirs = basePath, samples = input$sampleName))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
} else if (input$uploadChoice == "seqc") {
id <- paste0("newSampleSEQ", allImportEntries$id_count)
entry <- list(type="seqc", id = id, params=list(seqcDirs = basePath, prefix = input$sampleID, samples = input$sampleName))
updateTextInput(session, "sampleID", value = "")
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
} else if (input$uploadChoice == "optimus") {
id <- paste0("newSampleOpt", allImportEntries$id_count)
entry <- list(type="optimus", id = id, params=list(OptimusDirs = basePath, samples = input$sampleName))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
}
addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from CR3
observeEvent(input$addFilesImport_custom, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="cellRanger3_files", id = id, params=list(assayFile = input$countsfile_custom$datapath, annotFile = input$annotFile_custom$datapath,
featureFile = input$featureFile_custom$datapath,
summaryFile = input$summaryFile_custom$datapath,
sampleName = input$sampleNameCR,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom$datapath)
}
if (!is.null(input$annotFile_custom$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom$datapath)
}
if (!is.null(input$featureFile_custom$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom$datapath)
}
if (!is.null(input$summaryFile_custom$datapath)) {
summaryFileCol <- paste0("Metrics: ", input$summaryFile_custom$datapath)
}
if (!is.null(input$sampleNameCR)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameCR)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameCR)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from starSolo
observeEvent(input$addFilesImport_custom_starSolo, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="starSolo_files", id = id, params=list(assayFile = input$countsfile_custom_starSolo$datapath, annotFile = input$annotFile_custom_starSolo$datapath,
featureFile = input$featureFile_custom_starSolo$datapath,
sampleName = input$sampleNameSS,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom_starSolo$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom_starSolo$datapath)
}
if (!is.null(input$annotFile_custom_starSolo$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom_starSolo$datapath)
}
if (!is.null(input$featureFile_custom_starSolo$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom_starSolo$datapath)
}
if (!is.null(input$sampleNameSS)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameSS)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameSS)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from busTools
observeEvent(input$addFilesImport_custom_busTools, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="busTools_files", id = id, params=list(assayFile = input$countsfile_custom_busTools$datapath, annotFile = input$annotFile_custom_busTools$datapath,
featureFile = input$featureFile_custom_busTools$datapath,
sampleName = input$sampleNameBT,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom_busTools$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom_busTools$datapath)
}
if (!is.null(input$annotFile_custom_busTools$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom_busTools$datapath)
}
if (!is.null(input$featureFile_custom_busTools$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom_busTools$datapath)
}
if (!is.null(input$sampleNameSS)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameBT)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameBT)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from optimus
observeEvent(input$addFilesImport_custom_optimus, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="optimus_files", id = id, params=list(matrixLocation = input$matrix_custom_optimus$datapath,
colIndexLocation = input$colIndex_custom_optimus$datapath,
rowIndexLocation = input$rowIndex_custom_optimus$datapath,
cellMetricsLocation = input$cellMetrics_custom_optimus$datapath,
geneMetricsLocation = input$geneMetrics_custom_optimus$datapath,
emptyDropsLocation = input$emptyDrops_custom_optimus$datapath,
sampleName = input$sampleNameOP,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
matrixFileCol <- ""
colIndexFileCol <- ""
rowIndexFileCol <- ""
cellMetricsFileCol <- ""
geneMetricsFileCol <- ""
emptyDropsFileCol <- ""
sampleFileName <- ""
if (!is.null(input$matrix_custom_optimus$datapath)) {
matrixFileCol <- paste0("Matrix: ", input$matrix_custom_optimus$datapath)
}
if (!is.null(input$colIndex_custom_optimus$datapath)) {
colIndexFileCol <- paste0("colIndex: ", input$colIndex_custom_optimus$datapath)
}
if (!is.null(input$rowIndex_custom_optimus$datapath)) {
rowIndexFileCol <- paste0("rowIndex: ", input$rowIndex_custom_optimus$datapath)
}
if (!is.null(input$cellMetrics_custom_optimus$datapath)) {
cellMetricsFileCol <- paste0("cellMetrics: ", input$cellMetrics_custom_optimus$datapath)
}
if (!is.null(input$geneMetrics_custom_optimus$datapath)) {
geneMetricsFileCol <- paste0("geneMetrics: ", input$geneMetrics_custom_optimus$datapath)
}
if (!is.null(input$emptyDrops_custom_optimus$datapath)) {
emptyDropsFileCol <- paste0("emptyDrops: ", input$emptyDrops_custom_optimus$datapath)
}
if (!is.null(input$sampleNameOP)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameOP)
}
locCol <- paste(c(matrixFileCol, colIndexFileCol, rowIndexFileCol, cellMetricsFileCol, geneMetricsFileCol, emptyDropsFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameOP)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from seqc
observeEvent(input$addFilesImport_custom_seqc, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="seqc_files", id = id, params=list(readCountsLocation = input$readCounts_custom_seqc$datapath,
moleculeCountsLocation = input$moleculeCounts_custom_seqc$datapath,
barcodesLocation = input$barcodes_custom_seqc$datapath,
genesLocation = input$genes_custom_seqc$datapath,
sampleName = input$sampleNameSC,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
matrixFileCol <- ""
molFileCol <- ""
barcodesFileCol <- ""
genesFileCol <- ""
sampleFileName <- ""
if (!is.null(input$readCounts_custom_seqc$datapath)) {
matrixFileCol <- paste0("Matrix: ", input$readCounts_custom_seqc$datapath)
}
if (!is.null(input$moleculeCounts_custom_seqc$datapath)) {
molFileCol <- paste0("molCounts: ", input$moleculeCounts_custom_seqc$datapath)
}
if (!is.null(input$barcodes_custom_seqc$datapath)) {
barcodesFileCol <- paste0("barcodes: ", input$barcodes_custom_seqc$datapath)
}
if (!is.null(input$genes_custom_seqc$datapath)) {
genesFileCol <- paste0("genes: ", input$genes_custom_seqc$datapath)
}
if (!is.null(input$sampleNameSC)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameSC)
}
locCol <- paste(c(matrixFileCol, molFileCol, barcodesFileCol, genesFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameSC)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input
observeEvent(input$addFilesImport, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="files", id = id, params=list(assayFile = input$countsfile$datapath, annotFile = input$annotFile$datapath,
featureFile = input$featureFile$datapath, assayName = input$inputAssayType))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
if (!is.null(input$countsfile$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile$datapath)
}
if (!is.null(input$annotFile$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile$datapath)
}
if (!is.null(input$featureFile$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile$datapath)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$inputAssayType)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import an example input
observeEvent(input$addExampleImport, {
id <- paste0("newSampleExample", allImportEntries$id_count)
entry <- list(type="example", id = id, params=list(dataset = input$selectExampleData))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
scRNAseqDatasets <- c("fluidigm_pollen", "allen_tasic", "NestorowaHSCData")
tenxPbmcDatasets <- c("pbmc3k", "pbmc4k", "pbmc6k", "pbmc8k", "pbmc33k", "pbmc68k")
locCol <- ""
if (input$selectExampleData %in% scRNAseqDatasets) {
locCol <- "scRNA"
} else {
locCol <- "TENx"
}
addToGeneralSampleTable("example", id, locCol, input$selectExampleData)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import an RDS input
observeEvent(input$addRDSImport, {
id <- paste0("newSampleRDS", allImportEntries$id_count)
entry <- list(type="rds", id = id, params=list(rdsFile=input$rdsFile$datapath))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
addToGeneralSampleTable("rds", id, input$rdsFile$datapath, "")
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
observeEvent(input$backToStepOne, {
updateCollapse(session = session, "importUI",
open = "1. Add sample to import:")
updateRadioButtons(session = session, "uploadChoice", selected = "cellRanger3")
})
# Event handler for "Upload" button on import page
observeEvent(input$uploadData, withConsoleMsgRedirect(
msg = "Please wait while data is being imported. See console log for progress.",
{
if (length(allImportEntries$samples) == 0) {
stop("You have not selected any samples to import.")
}
sceObj <- importMultipleSources(allImportEntries)
if (input$combineSCEChoice == "addToExistingSCE") {
if(!is.null(vals$original)) {
sceList <- list(vals$original, sceObj)
vals$original <- combineSCE(sceList = sceList,
by.r = NULL,
by.c = Reduce(intersect, lapply(sceList, function(x) { colnames(colData(x))})),
combined = TRUE)
} else {
vals$original <- sceObj
}
} else {
vals$original <- sceObj
}
# clear table and empty reactive
for (entry in allImportEntries$samples) {
removeUI(selector = paste0("#", entry$id))
}
allImportEntries$samples <- list()
# Add sample variable if it was not included
if (!"sample" %in% names(colData(vals$original)) &&
!"Sample" %in% names(colData(vals$original))) {
sampleVar <- "sample"
# Let the sample name of all cells be "sample"
colData(vals$original)$sample = sampleVar
} else if ("sample" %in% names(colData(vals$original))) {
sampleVar <- "sample"
} else {
sampleVar <- "Sample"
}
if (!is.null(vals$original)) {
vals$counts <- vals$original
#store assayType information in the metadata
# if (!"assayType" %in% names(metadata(vals$counts))) {
# vals$counts <- expSetDataTag(
# inSCE = vals$counts,
# assayType = "raw",
# assays = assayNames(vals$counts))
# }
if (any(duplicated(rownames(vals$counts)))) {
warning("Duplicated rownames detected, making them unique...")
vals$counts <- dedupRowNames(vals$counts)
}
# add feature ids (from rownames) to rowData if rowData is empty
if (ncol(rowData(vals$counts)) < 1){
if(!is.null(rownames(vals$counts))){
rowData(vals$counts) <- S4Vectors::DataFrame(feature_id = rownames(vals$counts))
}
}
# ToDo: Remove these automatic updates and replace with
# observeEvents functions that activate upon the tab selection
updateColDataNames()
updateSelectInput(session, "qcSampleSelect", selected = sampleVar)
updateFeatureAnnots()
updateNumSamples()
updateAssayInputs()
updateGeneNames()
updateReddimInputs()
shinyjs::show(id="annotationData")
js$enableTabs()
updateGeneSetSelection()
} else {
shinyalert::shinyalert("Error!", "The data upload failed!",
type = "error")
}
vals$gsvaRes <- NULL
vals$vamRes <- NULL
vals$vamResults <- NULL
vals$gsvaResults <- NULL
vals$gsvaLimma <- NULL
vals$vamScore <- NULL
vals$gsvaScore <- NULL
vals$visplotobject <- NULL
vals$enrichRes <- NULL
vals$dimRedPlot <- NULL
vals$dimRedPlot_geneExp <- NULL
vals$dendrogram <- NULL
vals$pcX <- NULL
vals$pcY <- NULL
vals$batchRes <- NULL
vals$hvgCalculated <- list(status = FALSE, method = NULL)
dbList <- getMSigDBTable()
geneSetDBChoices <- formatGeneSetDBChoices(dbIDs = dbList$ID, dbCats = dbList$Category_Description)
updateCheckboxGroupInput(session, 'geneSetDB', choices = geneSetDBChoices)
updateSeuratUIFromRDS(vals$counts)
cleanGSTable()
updateHVGMetricSelection()
updateHVGListSelection()
updateDEAnalysisNames()
updateEnrichRAnalysisNames()
updateFeatureDisplaySelect()
updateTSCANUICollapse()
# TODO: There are more things that need to be cleaned when uploading new
# dataset, including any plots, tables that are origined from the old
# datasets. Otherwise, errors may pop out when Shiny listens to the new
# object but cannot find the old result.
updateCollapse(session = session, "importUI",
open = "3. Data summary:",
close = "2. Create dataset:",
style = list("2. Create dataset:" = "success"))
callModule(module = nonLinearWorkflow, id = "nlw-import", parent = session,
qcf = TRUE)
}))
updateSeuratUIFromRDS <- function(inSCE){
if(!is.null(metadata(inSCE)$seurat$plots)){
showNotification(HTML("Computation from Seurat Report detected in the input object, therefore the toolkit will now populate the Seurat tab with computated data & plots for further inspection. Click on the button below to directly go the the Seurat tab of the toolkit now! <br><br>"),
type = "message", duration = 0, action = actionBttn(
inputId = "goToSeurat",
label = "Go to Seurat Curated Workflow",
style = "bordered",
color = "royal",
size = "s",
icon = icon("arrow-right")
), id = "goSeuratNotification")
#Normalize Data
shinyjs::enable(selector = "#SeuratUI > div[value='Normalize Data']")
updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
normalizeParams <- metadata(vals$counts)$seurat$sctk$report$normalizeParams
updateSelectInput(session, "normalization_method", selected = normalizeParams$normalizationMethod)
updateTextInput(session, "scale_factor", value = normalizeParams$scaleFactor)
#Scale Data
shinyjs::enable(selector = "div[value='Scale Data']")
updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
scaleParams <- metadata(vals$counts)$seurat$sctk$report$scaleParams
updateSelectInput(session, "model.use", selected = scaleParams$model)
#HVG
hvgParams <- metadata(vals$counts)$seurat$sctk$report$hvgParams
output$plot_hvg <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratHVG(vals$counts, labelPoints = hvgParams$labelPoints))
})
})
shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
updateSelectInput(session, "hvg_method", selected = hvgParams$hvgMethod)
updateTextInput(session, "hvg_no_features", value = hvgParams$hvgNumber)
updateTextInput(session, "hvg_no_features_view", value = hvgParams$labelPoints)
#DR
pcaParams <- metadata(vals$counts)$seurat$sctk$report$pcaParams
shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
shinyjs::show(selector = ".seurat_pca_plots")
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca")
)
), select = TRUE)
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
panel(heading = "Elbow Plot",
plotlyOutput(outputId = "plot_elbow_pca")
)
))
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
plotlyOutput(outputId = "plot_jackstraw_pca")
)
))
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionButton(inputId = "plot_heatmap_pca_button", "Plot")
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
)
)
))
# output$plot_pca <- renderPlotly({
# plotly::ggplotly(metadata(inSCE)$seurat$plots$pca)
# })
output$plot_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts, useReduction = "pca"))
})
})
#updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))
# output$plot_elbow_pca <- renderPlotly({
# metadata(inSCE)$seurat$plots$elbow
# })
#update parameters from seurat report
output$plot_elbow_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratElbow(inSCE = vals$counts))
})
})
output$pca_significant_pc_output <- renderText({
isolate({
paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", pcaParams$significant_PC," </span> </p> <hr>")
})
})
# output$plot_jackstraw_pca <- renderPlotly({
# plotly::ggplotly(metadata(inSCE)$seurat$plots$jackstraw)
# })
output$plot_jackstraw_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratJackStraw(vals$counts))
})
})
# output$plot_heatmap_pca <- renderPlot({
# metadata(inSCE)$seurat$plots$heatmap
# })
updateTextInput(session, "pca_no_components", value = pcaParams$nPCs)
updateMaterialSwitch(session, "pca_compute_jackstraw", value = TRUE)
updateNumericInput(session, "pca_significant_pc_counter", value = pcaParams$significant_PC)
pcHeatmapParams <- metadata(inSCE)$seurat$plots$heatmap
pcHeatmapParams$inSCE <- vals$counts
output$plot_heatmap_pca <- renderPlot({
isolate({
do.call("runSeuratHeatmap", pcHeatmapParams)
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
#2D-Embedding
shinyjs::enable(selector = "#SeuratUI > div[value='2D-Embedding']")
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
# output$plot_tsne <- renderPlotly({
# metadata(inSCE)$seurat$plots$tsne
# })
#
# output$plot_umap <- renderPlotly({
# metadata(inSCE)$seurat$plots$umap
# })
output$plot_tsne <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne"))
})
})
output$plot_umap <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap"))
})
})
#Clustering
clusterParams <- metadata(vals$counts)$seurat$sctk$report$clusterParams
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca_clustering")
)
), select = TRUE
)
output$plot_pca_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "pca", showLegend = TRUE))
})
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotlyOutput(outputId = "plot_tsne_clustering")
)
)
)
output$plot_tsne_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne", showLegend = TRUE))
})
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotlyOutput(outputId = "plot_umap_clustering")
)
)
)
output$plot_umap_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap", showLegend = TRUE))
})
shinyjs::show(selector = ".seurat_clustering_plots")
updateNumericInput(session, "resolution_clustering", value = clusterParams$resolution)
#Find Markers
shinyjs::enable(selector = "#SeuratUI > div[value='Find Markers']")
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
shinyjs::show(selector = ".seurat_findmarker_table")
shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
shinyjs::show(selector = ".seurat_findmarker_plots")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerRidgePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerFeaturePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerDotPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "findMarkerHeatmapPlot")
)
)
)
)
)
)
showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "Ridge Plot")
shinyjs::show(selector = ".seurat_findmarker_plots")
groupHeatmapParams <- metadata(vals$counts)$seurat$plots$groupHeatmapParams
groupHeatmapParams$inSCE <- vals$counts
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
do.call("plotSeuratGenes", groupHeatmapParams)
})
})
output$findMarkerHeatmapPlotFullTopText <- renderUI({
h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
})
updateSelectInput(session, "seuratFindMarkerSelectPhenotype", choices = colnames(colData(vals$counts)), selected = metadata(vals$counts)$seurat$plots$group)
vals$fts <- callModule(
module = filterTableServer,
id = "filterSeuratFindMarker",
dataframe = metadata(vals$counts)$seurat$plots$top9,
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
#Downstream Analysis
shinyjs::show(selector = "div[value='Downstream Analysis']")
updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
}
}
observeEvent(input$goToSeurat,{
updateTabsetPanel(session, "navbar",
selected = "Seurat")
removeNotification(id = "goSeuratNotification", session = session)
})
observeEvent(input$importFeatureDipSet, {
if (!is.null(vals$counts)) {
withBusyIndicatorServer("importFeatureDipSet", {
selected <- NULL
if (!stringr::word(input$importFeatureDispOpt, 1) == "Default") {
featureName <- word(input$importFeatureDispOpt, 1)
selected <- featureName
}
if (!stringr::word(input$importFeatureNamesOpt, 1) == "Default") {
featureID <- word(input$importFeatureNamesOpt, 1)
rownames(vals$counts) <- rowData(vals$counts)[[featureID]]
}
vals$counts <- setSCTKDisplayRow(vals$counts, selected)
updateFeatureDisplaySelect(selected = selected)
})
}
})
updateFeatureDisplaySelect <- function(selected = NULL, updateOptions = FALSE)
{
if (is.null(selected)) {
if (!is.null(vals$counts))
selected <- metadata(vals$counts)$featureDisplay
if (is.null(selected))
selected <- "Rownames (Default)"
}
updateSelectInput(session, "hvgPlotFeatureDisplay", selected = selected)
updateSelectInput(session, "fmHMFeatureDisplay", selected = selected)
updateSelectInput(session, "deHMrowLabel", selected = selected)
updateSelectInput(session, "deVolcFeatureDisplay", selected = selected)
updateSelectInput(session, "deVioLabel", selected = selected)
updateSelectInput(session, "deRegLabel", selected = selected)
updateSelectInput(session, "tscanDEFeatureDisplay", selected = selected)
updateSelectInput(session, "plotTSCANClusterDEG_featureDisplay",
selected = selected)
updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
selected = selected)
}
#-----------#
# Gene Sets ####
#-----------#
numGS <- reactiveValues(id_count = 0)
addToGSTable <- function(nameCol, locCol) {
numGS$id_count <- numGS$id_count + 1
id <- paste0("geneSet", numGS$id_count)
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
insertUI(
selector = "#newGSImport",
ui = fluidRow(
id = id,
tags$style(HTML(fluidRowStyle)),
column(3, nameCol),
column(9, locCol),
)
)
}
vals$defaultQCGS <- c("None" = "none",
"Human Mitochondrial Genes (Ensembl)" = "he",
"Human Mitochondrial Genes (Symbol)" = "hs",
"Mouse Mitochondrial Genes (Ensembl)" = "me",
"Mouse Mitochondrial Genes (Symbol)" = "ms")
cleanGSTable <- function() {
for (i in seq(numGS$id_count)) {
removeUI(
selector = paste0("#geneSet", i)
)
}
numGS$id_count <- 0
if (!is.null(vals$counts)) {
existGS <- sctkListGeneSetCollections(vals$counts)
if (length(existGS) > 0) {
for (i in existGS) {
addToGSTable(i, "SCE Object")
}
updateSelectInput(session, "gsExisting", choices = c("None", existGS))
names(existGS) <- existGS
updateSelectInput(session, "QCMgeneSets", choices =c("None", existGS),
selected = "None")
shinyjs::show(id = "gsAddToExisting", anim = FALSE)
} else {
shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
}
} else {
updateSelectInput(session, "gsExisting", choices = "None")
updateSelectInput(session, "QCMgeneSets", choices = "None")
shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
}
}
handleGSPasteOption <- function(byParam) {
if (!nzchar(input$geneSetText)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else if ((!nzchar(input$gsCollectionNameText)) && (input$gsExisting == "None")) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
setList <- formatGeneSetList(input$geneSetText)
if (nzchar(input$gsCollectionNameText)) {
vals$counts <- importGeneSetsFromList(vals$counts,
setList,
by = byParam,
collectionName = input$gsCollectionNameText)
addToGSTable(input$gsCollectionNameText, "Paste-In")
} else if (input$gsExisting != "None") {
vals$counts <- importGeneSetsFromList(vals$counts,
setList,
by = byParam,
collectionName = input$gsExisting)
addToGSTable(input$gsExisting, "Paste-In")
}
}
}
observeEvent(input$uploadGS, withConsoleMsgRedirect(
msg = "Please wait while gene sets are being imported. See console log for progress.",
{
byParam = NULL
if (input$gsByParam != "None") {
byParam <- input$gsByParam
}
if (input$geneSetSourceChoice == "gsGMTUpload") {
if (is.null(input$geneSetGMT)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else if (!nzchar(input$gsCollectionNameGMT)){
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
vals$counts <- importGeneSetsFromGMT(vals$counts,
input$geneSetGMT$datapath,
by = byParam,
collectionName = input$gsCollectionNameGMT)
addToGSTable(input$gsCollectionNameGMT, input$geneSetGMT$datapath)
}
} else if (input$geneSetSourceChoice == "gsDBUpload") {
if (is.null(input$geneSetDB)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
vals$counts <- importGeneSetsFromMSigDB(vals$counts,
input$geneSetDB,
by = byParam)
for(i in input$geneSetDB){
# Handling multiple selections from the checkboxInput
addToGSTable(i, "Database")
}
}
} else if (input$geneSetSourceChoice == "gsMito") {
vals$counts <- importMitoGeneSet(vals$counts,
reference = input$geneSetMitoSpecies,
id = input$geneSetMitoID,
by = byParam,
collectionName = input$geneSetMitoName)
addToGSTable(input$geneSetMitoName, "SCTK Curated Geneset")
} else if (input$geneSetSourceChoice == "gsPasteUpload") {
handleGSPasteOption(byParam)
}
updateGeneSetSelection()
shinyjs::show(id = "gsAddToExisting", anim = FALSE)
}))
#----#
# QC #####
#----#
# Hide and show parameters for QC functions
shinyjs::onclick("QCMetrics", shinyjs::toggle(id = "QCMetricsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("decontX", shinyjs::toggle(id = "decontXParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("soupX", shinyjs::toggle(id = "soupXParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("scDblFinder", shinyjs::toggle(id = "scDblFinderParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("cxds", shinyjs::toggle(id = "cxdsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("bcds", shinyjs::toggle(id = "bcdsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("cxds_bcds_hybrid", shinyjs::toggle(id = "cxds_bcds_hybridParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("scrublet", shinyjs::toggle(id = "scrubletParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("doubletFinder", shinyjs::toggle(id = "doubletFinderParams",
anim = FALSE), add = TRUE)
qc_choice_list <- list("scDblFinder", "cxds", "bcds",
"cxds_bcds_hybrid", "decontX", "soupX", "QCMetrics", "scrublet", "doubletFinder")
# holds all the input ids for the QC algorithm parameters by algorithm name
qc_input_ids <- list(scDblFinder = list(nNeighbors="DCnNeighbors", simDoublets="DCsimDoublets"),
cxds = list(ntop="CXntop", binThresh="CXbinThresh", verb="CXverb", retRes="CXretRes"),#, estNdbl="CXestNdbl"),
bcds = list(ntop="BCntop", srat="BCsrat", nmax="BCnmax", verb="BCverb", retRes="BCretRes", varImp="BCvarImp"),#, estNdbl="BCestNdbl"),
cxds_bcds_hybrid = list(cxdsArgs=list(ntop="CX2ntop", binThresh="CX2binThresh", retRes="CX2retRes"),
bcdsArgs=list(ntop="BC2ntop", srat="BC2srat", nmax="BC2nmax", retRes="BC2retRes", varImp="BC2varImp"),
verb="CXBCverb"),#, estNdbl="CXBCestNdbl"),
decontX = list(maxIter="DXmaxIter", estimateDelta="DXestimateDelta", convergence="DXconvergence",
iterLogLik="DXiterLogLik", varGenes="DXvarGenes", dbscanEps="DXdbscanEps", verbose="DXverbose"),
soupX = list(cluster="soupXCluster", tfidfMin="soupXTfidfMin", soupQuantile="soupXQuantile", maxMarkers="soupXMaxMarkers", rhoMaxFDR="soupXRhoMaxFDR",
priorRho="soupXPriorRho", priorRhoStdDev="soupXPriorRhoStdDev", forceAccept="soupXForceAccept", adjustMethod="soupXAdjustMethod",
roundToInt="soupXRoundToInt", tol="soupXTol", pCut="soupXPCut"),
doubletFinder = list(seuratNfeatures="DFseuratNfeatures", seuratRes="DFseuratRes", formationRate="DFformationRate", verbose="DFverbose"),
scrublet = list(simDoubletRatio="SsimDoubletRatio", nNeighbors="SnNeighbors", minDist="SminDist", expectedDoubletRate="SexpectedDoubletRate",
stdevDoubletRate='SstdevDoubletRate', syntheticDoubletUmiSubsampling="SsyntheticDoubletUmiSubsampling",
useApproxNeighbors="SuseApproxNeighbors", distanceMetric="SdistanceMetric", getDoubletNeighborParents="SgetDoubletNeighborParents", minCounts="SminCounts",
minCells="SminCells", minGeneVariabilityPctl="SminGeneVariabilityPctl", logTransform="SlogTransform", meanCenter="SmeanCenter",
normalizeVariance="SnormalizeVariance", nPrinComps="SnPrinComps", tsneAngle="StsneAngle", tsnePerplexity="StsnePerplexity", verbose="Sverbose")
)
# to keep track of whether an algo has already been run
qc_algo_status = reactiveValues(scDblFinder=NULL, cxds=NULL, bcds=NULL, cxds_bcds_hybrid=NULL, decontX=NULL, soupX=NULL,
QCMetrics=NULL, scrublet=NULL, doubletFinder=NULL)
qc_plot_ids = reactiveValues(scDblFinder="DCplots", cxds="CXplots", bcds="BCplots", cxds_bcds_hybrid="CXBCplots", decontX="DXplots",
soupX="SoupXPlots", QCMetrics="QCMplots", scrublet="Splots", doubletFinder="DFplots")
# event handlers to open help pages for each qc algorithm
observeEvent(input$DXhelp, {
showModal(decontXHelpModal())
})
observeEvent(input$SoupXhelp, {
showModal(soupXHelpModal())
})
observeEvent(input$CXhelp, {
showModal(cxdsHelpModal())
})
observeEvent(input$BChelp, {
showModal(bcdsHelpModal())
})
observeEvent(input$CXBChelp, {
showModal(cxdsBcdsHybridHelpModal())
})
observeEvent(input$DFhelp, {
showModal(doubletFinderHelpModal())
})
observeEvent(input$Shelp, {
showModal(scrubletHelpModal())
})
observeEvent(input$DChelp, {
showModal(scDblFinderHelpModal())
})
observeEvent(input$QCMhelp, {
showModal(QCMHelpModal())
})
observeEvent(input$QCImportGS, {
showTab(inputId = "navbar",
target = "Import Gene Sets",
select = TRUE,
session = session)
})
# format the parameters for decontX
prepDecontXParams <- function(paramsList) {
inputIds <- qc_input_ids[["decontX"]]
dxParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
dxParams[[key]] = input[[inputIds[[key]]]]
}
# put in the delta params (c-bind the two priors)
dxParams[["delta"]] <- c(input$DXnativePrior, input$DXcontPrior)
# add to master params list
paramsList[["decontX"]] = dxParams
return(paramsList)
}
# format the parameters for SoupX
prepSoupXParams <- function(paramsList) {
inputIds <- qc_input_ids[["soupX"]]
soupXParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
soupXParams[[key]] = input[[inputIds[[key]]]]
}
soupXParams[["contaminationRange"]] <- c(input$soupXContRangeLow,
input$soupXContRangeHigh)
if (soupXParams[["cluster"]] == "None") {
soupXParams[["cluster"]] <- NULL
}
# add to master params list
paramsList[["decontX"]] = soupXParams
return(paramsList)
}
# format the parameters for doubletFinder
prepDoubletFinderParams <- function(paramsList) {
inputIds <- qc_input_ids[["doubletFinder"]]
dfParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
dfParams[[key]] = input[[inputIds[[key]]]]
}
# put in the seuratPcs param (range from 1 to given value)
dfParams[["seuratPcs"]] <- 1:input$DFseuratPcs
# add to master params list
paramsList[["doubletFinder"]] = dfParams
return(paramsList)
}
qcInputExists <- function() {
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
return(TRUE)
}
}
return(FALSE)
}
updateQCPlots <- function() {
# get selected sample from run QC section
if (!is.null(vals$original)) {
qcSample <- input$qcSampleSelect
if (qcSample == "None") {
qcSample <- NULL
} else {
qcSample <- colData(vals$original)[,input$qcSampleSelect]
}
# build list of selected algos
algoList = list()
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
algoList <- c(algoList, algo)
}
}
# only run runUMAP if there are no reducedDimNames
# redDimName <- input$qcPlotRedDim
# show the tabs for the result plots output[[qc_plot_ids[[a]]]]
showQCResTabs(vals, algoList, qc_algo_status, qc_plot_ids)
arrangeQCPlots(vals$original, input, output, algoList,
colData(vals$original)[[input$qcSampleSelect]], qc_plot_ids,
qc_algo_status, input$QCUMAPName)
uniqueSampleNames = unique(colData(vals$original)[[input$qcSampleSelect]])
for (algo in algoList) {
qc_algo_status[[algo]] <- list(self="done")
if (length(uniqueSampleNames) > 1) {
for (s in uniqueSampleNames) {
qc_algo_status[[algo]][[s]] = TRUE
}
}
}
}
}
observeEvent(input$runQC, withConsoleMsgRedirect(
msg = "Please wait while QC metrics are being calculated. See console log for progress.",
{
if (!qcInputExists()) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noSelected", tags$b("Please select at least one algorithm.", style = "color: red;"))
)
} else if (is.null(vals$original)) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noSCE", tags$b("Please upload a sample first.", style = "color: red;"))
)
} else if (is.null(input$qcAssaySelect)) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noQCAssay", tags$b("Please select an assay.", style = "color: red;"))
)
} else {
removeUI(
selector = "#noSelected"
)
removeUI(
selector = "#noSCE"
)
removeUI(
selector = "#noQCAssay"
)
useAssay <- input$qcAssaySelect
qcSample <- colData(vals$original)[,input$qcSampleSelect]
if (length(qcSample)==1 && qcSample == "None") {
qcSample <- NULL
}
# Handle mitochondrial gene set selection
mgsRef <- NULL
mgsId <- NULL
mgsLoc <- NULL
if (input$QCMito != "None") {
if (input$QCMito == "he") {
# Import Human Mito Ensembl
mgsRef <- "human"
mgsId <- "ensembl"
} else if (input$QCMito == "hs") {
# Import Human Mito Symbol
mgsRef <- "human"
mgsId <- "symbol"
} else if (input$QCMito == "me") {
# Import Mouse Mito Ensembl
mgsRef <- "mouse"
mgsId <- "ensembl"
} else if (input$QCMito == "ms") {
# Import Mouse Mito Symbol
mgsRef <- "mouse"
mgsId <- "symbol"
}
mgsLoc <- "rownames"
}
# Handle another genesetCollection selection
qcCollName <- NULL
if (input$QCMgeneSets != "None") {
qcCollName <- input$QCMgeneSets
}
algoList = list()
paramsList <- list()
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
algoList <- c(algoList, algo)
# use the specific prep functions for decontX, SoupX and doubletFinder
if (algo == "decontX") {
paramsList <- prepDecontXParams(paramsList)
next
}
if (algo == "soupX") {
paramsList <- prepSoupXParams(paramsList)
next
}
if (algo == "doubletFinder") {
paramsList <- prepDoubletFinderParams(paramsList)
next
}
# everything else can go through the rest of the loop
inputIds <- qc_input_ids[[algo]]
algoParams <- list()
for (key in names(inputIds)) {
if(typeof(inputIds[[key]]) == "list") {
paramSubList <- list()
for (key2 in names(inputIds[[key]])) {
paramSubList[[key2]] <- input[[inputIds[[key]][[key2]]]]
}
algoParams[[key]] = paramSubList
} else {
algoParams[[key]] = handleEmptyInput(input[[inputIds[[key]]]])
}
}
paramsList[[algo]] = algoParams
}
}
# run selected cell QC algorithms
vals$original <- runCellQC(inSCE = vals$original,
algorithms = algoList,
sample = qcSample,
collectionName = qcCollName,
mitoRef = mgsRef,
mitoIDType = mgsId,
mitoGeneLocation = mgsLoc,
useAssay = input$qcAssaySelect,
paramsList = paramsList)
# Only copy the newly generated colData variables to vals$counts, but
# not replacing the vals$counts. vals$counts might have already become
# a subset.
vals$counts <- passQCVar(vals$original, vals$counts, algoList)
updateColDataNames()
updateAssayInputs()
# redDimList <- strsplit(reducedDimNames(vals$original), " ")
# run runUMAP if doublet/ambient RNA detection conducted
#umap generated during soupX, skip for now
if (length(intersect(c("scDblFinder", "cxds", "bcds",
"cxds_bcds_hybrid", "decontX", #"soupX",
"scrublet", "doubletFinder"), algoList)) > 0) {
message(paste0(date(), " ... Running 'UMAP'"))
vals$original <- runUMAP(inSCE = vals$original,
sample = qcSample,
useAssay = input$qcAssaySelect,
useReducedDim = NULL,
nNeighbors = input$UnNeighbors,
nIterations = input$UnIterations,
alpha = input$Ualpha,
minDist = input$UminDist,
spread = input$Uspread,
initialDims = input$UinitialDims,
reducedDimName = input$QCUMAPName,
seed = input$Useed)
}
message(paste0(date(), " ... QC Complete"))
updateQCPlots()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-qcf", parent = session,
nbc = TRUE, cw = TRUE, cv = TRUE)
}
delay(500, removeNotification(id = "qcNotification"))
}))
#-----------#
# FILTERING #####
#-----------#
shinyjs::onclick("colGT", shinyjs::toggle(id = "filterThreshGT",
anim = FALSE), add = TRUE)
shinyjs::onclick("colLT", shinyjs::toggle(id = "filterThreshLT",
anim = FALSE), add = TRUE)
filteringParams <- reactiveValues(params = list(), id_count = 0)
rowFilteringParams <- reactiveValues(params = list(), id_count = 0)
observeEvent(input$addFilteringParam, {
if (!is.null(vals$original)) {
showModal(filteringModal(colNames = names(colData(vals$original))))
}
})
observeEvent(input$addRowFilteringParam, {
if (!is.null(vals$original) &&
!is.null(names(assays(vals$original)))) {
showModal(rowFilteringModal(assayInput = names(assays(vals$original))))
}
})
observeEvent(input$filterColSelect, {
# prep the modal - remove the threshold div and hide the categorical option
shinyjs::hide("convertFilterType")
removeUI(selector = "#newThresh")
removeUI(selector = "div:has(>> #convertToCat)")
# check if column contains numerical values
isNum <- is.numeric(vals$original[[input$filterColSelect]][0])
if (length(vals$original[[input$filterColSelect]]) > 0) {
if (isTRUE(isNum)) {
# (from partials) insertUI for choosing greater than and less than params
addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
# if less than 25 unique categories, give categorical option
if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
insertUI(
selector = "#convertFilterType",
ui = checkboxInput("convertToCat", "Convert to categorical filter?")
)
shinyjs::show("convertFilterType")
}
} else { # if non-numerical values, create checkbox input
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh",
checkboxGroupInput("filterThresh", "Please select which columns to keep:",
choices = as.vector(unique(vals$original[[input$filterColSelect]])),
),
)
)
}
} else { # if no values in column, show error
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh", tags$b("This column does not have any filtering criteria", style = "color: red;"))
)
}
})
observeEvent(input$convertToCat, {
if (!is.null(input$filterColSelect)) {
removeUI(selector = "#newThresh")
if (input$convertToCat) {
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh",
checkboxGroupInput("filterThresh", "Please select which columns to keep:",
choices = as.vector(unique(vals$original[[input$filterColSelect]])),
)
)
)
} else {
addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
shinyjs::show("convertFilterType")
}
}
}
})
observeEvent(input$filterAssaySelect, {
removeUI(selector = "#newThresh")
insertUI(
selector = "#rowFilterCriteria",
ui = tags$div(id="newThresh",
numericInput("filterThreshX", "Keep features with this many counts:", 0),
numericInput("filterThreshY", "In at least this many cells:", 0),
)
)
})
observeEvent(input$filtModalOK, {
if (is.null(input$filterThresh) && is.null(input$filterThreshGT) && is.null(input$filterThreshLT)) {
showModal(filteringModal(failed=TRUE, colNames = names(colData(vals$original))))
} else {
id <- paste0("filteringParam", filteringParams$id_count)
# figure out which options the user selected
criteriaGT <- NULL
criteriaLT <- NULL
categoricalCol = FALSE
if (isTRUE(input$colGT)) {
criteriaGT = input$filterThreshGT
}
if (isTRUE(input$colLT)) {
criteriaLT = input$filterThreshLT
}
if (!is.null(input$filterThresh)) {
categoricalCol = TRUE
}
if (isTRUE(input$colLT) && isTRUE(input$colGT)) {
if (criteriaGT > criteriaLT) {
insertUI(
selector = "#filterCrErrors",
ui = wellPanel(id = "voidRange",
tags$b("Please set a valid range.",
style = "color: red;"))
)
return()
}
}
# new row in parameters table
addToColFilterParams(name = input$filterColSelect,
categorial = categoricalCol,
criteria = input$filterThresh,
criteriaGT = criteriaGT,
criteriaLT = criteriaLT,
id = id,
paramsReactive = filteringParams)
threshStr <- ""
if (isTRUE(categoricalCol)) {
threshStr <- paste(input$filterThresh, collapse = ', ')
} else {
if (is.null(criteriaGT)) {
threshStr <- sprintf("< %.5f", input$filterThreshLT)
} else if (is.null(criteriaLT)) {
threshStr <- sprintf("> %.5f", input$filterThreshGT)
} else {
threshStr <- sprintf("> %.5f & < %.5f", input$filterThreshGT, input$filterThreshLT)
}
}
make3ColTableRow("#newFilteringParams", id, input$filterColSelect, threshStr)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in filteringParams$params) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
filteringParams$params <- filteringParams$params[toRemove]
})
removeModal()
}
})
observeEvent(input$rowFiltModalOK, {
if ((is.null(input$filterThreshX)) || (is.null(input$filterThreshY)) || (is.null(input$filterAssaySelect))) {
showModal(rowFilteringModal(failed=TRUE, assayInput = names(assays(vals$original))))
} else {
id <- paste0("rowFilteringParam", rowFilteringParams$id_count)
# new row in parameters table
threshStr <- sprintf("> %i counts in > %i cells", input$filterThreshX, input$filterThreshY)
addToRowFilterParams(input$filterAssaySelect, input$filterThreshX, input$filterThreshY, id, rowFilteringParams)
make3ColTableRow("#newRowFilteringParams", id, input$filterAssaySelect, threshStr)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in rowFilteringParams$params) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
rowFilteringParams$params <- rowFilteringParams$params[toRemove]
})
removeModal()
}
})
observeEvent(input$clearAllFilters, {
for (entry in filteringParams$params) {
removeUI(selector = paste0("#", entry$id))
}
filteringParams$params <- list()
})
observeEvent(input$clearAllRowParams, {
for (entry in rowFilteringParams$params) {
removeUI(selector = paste0("#", entry$id))
}
rowFilteringParams$params <- list()
})
filterSCE <- function(inSCE, colFilter, rowFilter) {
if (!is.null(colFilter)) {
# handle column filtering (pull out the criteria strings first)
colInput <- formatFilteringCriteria(colFilter$params)
if (length(colInput) > 0) {
inSCE <- subsetSCECols(inSCE, colData = colInput)
}
}
if (!is.null(rowFilter)) {
# handle row filtering (enter information as rows first, then pull out
# criteria strings)
rowInput <- formatFilteringCriteria(rowFilter$params)
if (length(rowInput) > 0) {
inSCE <- addRowFiltersToSCE(inSCE, rowFilter)
temp <- subsetSCERows(inSCE, rowData = rowInput, returnAsAltExp = FALSE)
if (nrow(temp) == 0) {
stop("This filter will clear all rows. Filter has not been applied.")
} else {
inSCE <- temp
}
}
}
return(inSCE)
}
observeEvent(input$filterSCE, withConsoleMsgRedirect(
msg = "Please wait while data is being filtered. See console log for progress.",
{
vals$counts <- filterSCE(vals$original, filteringParams, rowFilteringParams)
shinyjs::show(id="filteringSummary")
updateColDataNames()
updateReddimInputs()
updateFeatureAnnots()
updateAssayInputs()
# TODO: When new subset is being created and maybe replacing previous
# vals$counts, please find if any of the downstream UI need to be updated
# Show downstream analysis options
shinyjs::show(selector = ".nlw-qcf")
}))
#Render summary table
output$beforeFiltering <- renderTable({
req(vals$original)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$original,
useAssay = NULL,
sampleVariableName = sampleVar)
}, striped = TRUE, border = TRUE, align = "c", spacing = "l")
output$afterFiltering <- renderTable({
req(vals$counts)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$counts,
useAssay = NULL,
sampleVariableName = sampleVar)
}, striped = TRUE, border = TRUE, align = "c", spacing = "l")
#Render summary table
output$summarycontents <- DT::renderDataTable({
req(vals$counts)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$counts,
useAssay = NULL,
sampleVariableName = sampleVar)
})
observeEvent(input$filteredSample, {
output$filterSampleOptions <- renderUI({
isolate({
if (input$filteredSample != "none")({
if (length(unique(colData(vals$counts)[, input$filteredSample])) < 100){
L <- vector("list", 3)
L[[1]] <- renderText("Select samples to keep")
L[[2]] <- wellPanel(style = "overflow-y:scroll; max-height: 100px",
list(checkboxGroupInput("filterSampleChoices",
label = NULL,
choices = unique(colData(vals$counts)[, input$filteredSample]))),
tags$h5(tags$i("Note: the Reset button is in 'Delete Outliers' tab above."))
)
L[[3]] <- list(withBusyIndicatorUI(actionButton("runFilterSample", "Filter")))
return(L)
} else {
L <- list(renderText("Annotation must have fewer than 100 options"))
return(L)
}
}) else {
L <- list()
}
})
})
})
# Delete Data ####
output$reducedDimsList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts) &&
length(names(reducedDims(vals$counts))) > 0){
panel(heading = "ReducedDims",
checkboxGroupInput(
inputId = "checkboxRedDimToRemove",
label = NULL,
choices = names(reducedDims(vals$counts))
)
)
}
})
output$assaysList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)){
panel(heading = "Assays",
checkboxGroupInput(
inputId = "checkboxAssaysToRemove",
label = NULL,
choices = assayNames(vals$counts)
)
)
}
})
output$rowDataList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(colnames(rowData(vals$counts))) > 0){
panel(heading = "Row Annotation",
checkboxGroupInput(
inputId = "checkboxRowDataToRemove",
label = NULL,
choices = colnames(rowData(vals$counts))
)
)
}
})
output$colDataList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(colnames(colData(vals$counts))) > 0){
panel(heading = "Column Annotation",
checkboxGroupInput(
inputId = "checkboxColDataToRemove",
label = NULL,
choices = colnames(colData(vals$counts))
)
)
}
})
output$altExpList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(altExpNames(vals$counts)) > 0){
panel(heading = "Subsets",
checkboxGroupInput(
inputId = "checkboxAltExpToRemove",
label = NULL,
choices = altExpNames(vals$counts)
)
)
}
})
observeEvent(input$delRedDim, withConsoleMsgRedirect(
msg = "Please wait while selected data is being removed. See console log for progress.",
{
req(vals$counts)
if(length(input$checkboxAssaysToRemove) > 0){
for(i in seq(input$checkboxAssaysToRemove)){
expData(vals$counts, input$checkboxAssaysToRemove[i]) <- NULL
vals$counts <- expDeleteDataTag(vals$counts, input$checkboxAssaysToRemove[i])
message(paste0(date(), " ... Removed '", input$checkboxAssaysToRemove[i], "' assay."))
}
}
if(length(input$checkboxRedDimToRemove) > 0){
for(i in seq(input$checkboxRedDimToRemove)){
reducedDim(vals$counts, input$checkboxRedDimToRemove[i]) <- NULL
message(paste0(date(), " ... Removed '", input$checkboxRedDimToRemove[i], "' redDim."))
}
}
if(length(input$checkboxRowDataToRemove) > 0){
for(i in seq(input$checkboxRowDataToRemove)){
rowData(vals$counts)[[input$checkboxRowDataToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxRowDataToRemove[i], "' feature annotation."))
}
}
if(length(input$checkboxColDataToRemove) > 0){
for(i in seq(input$checkboxColDataToRemove)){
colData(vals$counts)[[input$checkboxColDataToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxColDataToRemove[i], "' sample annotation."))
}
}
if(length(input$checkboxAltExpToRemove) > 0){
for(i in seq(input$checkboxAltExpToRemove)){
altExps(vals$counts)[[input$checkboxAltExpToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxAltExpToRemove[i], "' subset."))
}
}
updateAssayInputs()
updateReddimInputs()
updateFeatureAnnots()
updateColDataNames()
}))
# Normalization ####
observeEvent(input$customNormalizeAssayMethodSelect, {
if(input$customNormalizeAssayMethodSelect == "LogNormalize"
|| input$customNormalizeAssayMethodSelect == "CLR"
|| input$customNormalizeAssayMethodSelect == "SCTransform"
|| input$customNormalizeAssayMethodSelect == "logNormCounts"){
updateAwesomeCheckbox(
session = session,
inputId = "customNormalizeOptionsTransform",
value = FALSE
)
}
})
output$normalizationDataTagUI <- renderUI({
req(vals$counts)
tag <- ""
if(input$normalizeAssayMethodSelect != "custom"){
if(input$normalizeAssayMethodSelect
%in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts", "NormalizeTotal")){
tag <- "transformed"
}
else{
tag <- "normalized"
}
if(input$normalizationScale){
tag <- "scaled"
}
}
else{
if(input$customNormalizeOptionsNormalize){
if(input$customNormalizeAssayMethodSelect
%in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts")){
tag <- "transformed"
}
else{
tag <- "normalized"
}
}
if(input$customNormalizeOptionsTransform){
tag <- "transformed"
}
if(input$customNormalizeOptionsScale){
tag <- "scaled"
}
}
return(tag)
})
output$normalizationNormalizeSelectedMethodUI <- renderUI({
req(vals$counts)
if(input$normalizeAssayMethodSelect != "custom"){
h5(input$normalizeAssayMethodSelect)
}
else{
NULL
}
})
observeEvent(input$modifyAssay, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
if (!(input$modifyAssaySelect %in% names(assays(vals$counts)))) {
stop("Assay does not exist!")
} else if (input$modifyAssayOutname == "") {
stop("Assay name cannot be empty!")
} else if (input$modifyAssayOutname %in% names(assays(vals$counts))) {
stop("Assay name already exists! Use another assay name!")
} else if(is.na(input$trimUpperValueAssay)
|| is.na(input$trimLowerValueAssay)){
stop("Upper or lower trim value cannot be empty!")
} else {
checkedOptions <- c(input$customNormalizeOptionsNormalize,
input$customNormalizeOptionsTransform,
input$customNormalizeOptionsPsuedocounts,
input$customNormalizeOptionsScale,
input$customNormalizeOptionsTrim)
if(!any(checkedOptions)){
stop("Must select at least one option!")
}
#Setting initial parameters
normalizeMethod <- NULL
transformMethod <- NULL
pseudocountsBefore <- NULL
pseudocountsAfter <- NULL
doScale <- input$customNormalizeOptionsScale
trimOptions <- NULL
if(input$customNormalizeOptionsNormalize)
normalizeMethod <- input$customNormalizeAssayMethodSelect
if(input$customNormalizeOptionsTransform)
transformMethod <- input$customNormalizeTransformOptions
if(input$customNormalizePseudoOptionsBefore)
pseudocountsBefore <- input$customNormalizePseudoValueBefore
if(input$customNormalizePseudoOptionsAfter)
pseudocountsAfter <- input$customNormalizePseudoValueAfter
if(input$customNormalizeOptionsTrim)
trimOptions <- c(input$trimUpperValueAssay, input$trimLowerValueAssay)
outAssayName <- input$modifyAssayOutname
useAssay <- input$modifyAssaySelect
args <- list(
inSCE = vals$counts,
useAssay = useAssay,
outAssayName = outAssayName,
normalizationMethod = normalizeMethod,
scale = doScale,
transformation = transformMethod,
pseudocountsBeforeNorm = pseudocountsBefore,
pseudocountsBeforeTransform = pseudocountsAfter,
trim = trimOptions
)
message(paste0(date(), " ... Starting normalization/transformation with selected assay: '", useAssay, "'."))
vals$counts <- do.call("runNormalization", args)
message(paste0(date(), " ... Ended normalization/transformation."))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
dr = TRUE, fs = TRUE)
}
}
))
observeEvent(input$normalizeAssay, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
if(!(input$normalizeAssaySelect %in% expDataNames(vals$counts))){
stop("Selected assay does not exist!")
} else if(input$normalizeAssayOutname == ""){
stop("Assay Name cannot be empty!")
} else if(input$normalizeAssayOutname %in% expDataNames(vals$counts)){
stop("Your selected Assay Name already exists! Try another Assay Name!")
} else if(input$normalizeAssaySelect == ""){
stop("Please select an assay before proceeding with normalization!")
} else if(is.na(as.numeric(input$normalizationScaleFactor))){
stop("Scaling factor must be a numeric non-empty value!")
} else{
#Setting initial parameters
normalizeMethod <- input$normalizeAssayMethodSelect
doScale <- input$normalizationScale
trimOptions <- NULL
scaleFactor <- input$normalizationScaleFactor
if(doScale && input$normalizationTrim)
trimOptions <- c(input$normalizationTrimUpper,
input$normalizationTrimLower)
outAssayName <- input$normalizeAssayOutname
useAssay <- input$normalizeAssaySelect
args <- list(
inSCE = vals$counts,
useAssay = useAssay,
outAssayName = outAssayName,
normalizationMethod = normalizeMethod,
scale = doScale,
seuratScaleFactor = scaleFactor,
trim = trimOptions
)
message(date(), " ... Starting normalization with selected assay: '",
useAssay, "'.")
vals$counts <- do.call("runNormalization", args)
message(paste0(date(), " ... Ended normalization."))
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
dr = TRUE, fs = TRUE)
}
}
))
observeEvent(input$normalizeAssayMethodSelect, {
if(input$normalizeAssayMethodSelect == "LogNormalize") {
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratLogNormalize")
} else if(input$normalizeAssayMethodSelect == "CLR"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratCLR")
} else if(input$normalizeAssayMethodSelect == "RC"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratRC")
} else if(input$normalizeAssayMethodSelect == "CPM"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScaterCPMCounts")
} else if(input$normalizeAssayMethodSelect == "logNormCounts"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScaterLogNormCounts")
} else if(input$normalizeAssayMethodSelect == "SCTransform"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratSCTransform")
} else if(input$normalizeAssayMethodSelect == "NormalizeTotal"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScanpyNormalizeTotal")
}
})
#-----------------------------------------------------------------------------
# Page 3: dimRed ####
#-----------------------------------------------------------------------------
output$dimRedNameUI <- renderUI({
defaultText <- paste(input$dimRedAssaySelect, input$dimRedPlotMethod,
sep = '_')
textInput('dimRedNameInput', "reducedDim Name:", defaultText)
})
output$dimRedNameUI_tsneUmap <- renderUI({
defaultText <- paste(input$dimRedAssaySelect_tsneUmap, input$dimRedPlotMethod_tsneUmap,
sep = '_')
textInput('dimRedNameInput_tsneUmap', "reducedDim Name:", defaultText)
})
observeEvent(input$updateHeatmap_dimRed, {
req(vals$counts)
if (!is.null(input$picker_dimheatmap_components_dimRed)) {
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plot_heatmap_dimRed <- renderPlot({
isolate({
singleCellTK:::.plotHeatmapMulti(
plots = vals$counts@metadata$seurat$heatmap_dimRed,
components = input$picker_dimheatmap_components_dimRed,
nCol = input$slider_dimheatmap_dimRed)
})
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plot_heatmap_dimRed <- renderPlot({
isolate({
singleCellTK:::.plotHeatmapMulti(
plots = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed,
components = input$picker_dimheatmap_components_dimRed,
nCol = input$slider_dimheatmap_dimRed)
})
})
}
}
session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
})
observeEvent(input$closeDropDownDimRedHeatmap, {
session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
})
observeEvent(input$runDimred, withConsoleMsgRedirect(
msg = "Please wait while dimensionality reduction is being computed. See console log for progress.",
{
req(vals$counts)
vals$runDimred$dimRedAssaySelect <- input$dimRedAssaySelect
if (vals$runDimred$dimRedAssaySelect %in% altExpNames(vals$counts)) {
dimRedUseAltExp <- vals$runDimred$dimRedAssaySelect
} else {
dimRedUseAltExp <- NULL
}
if (input$dimRedNameInput == "" || is.null(input$dimRedNameInput)){
stop("Please enter a reducedDim name")
}
dimrednamesave <- gsub(" ", "_", input$dimRedNameInput)
if (dimrednamesave %in% reducedDimNames(vals$counts)) {
stop("Specified reducedDim name already exist")
}
if (is.na(input$dimRedNumberDims) ||
input$dimRedNumberDims < 2) {
stop("Must specify a valid number of components for output")
}
useFeatureSubset <- input$dimRedHVGSelect
if (input$dimRedHVGSelect == "None") {
useFeatureSubset <- NULL
}
seed <- input$seed_dimRed
if (is.na(input$seed_dimRed)) {
seed <- NULL
}
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = vals$runDimred$dimRedAssaySelect,
useAltExp = dimRedUseAltExp,
method = input$dimRedPlotMethod,
useFeatureSubset = useFeatureSubset,
scale = input$dimRedScale,
nComponents = input$dimRedNumberDims,
reducedDimName = dimrednamesave,
seed = seed)
updateReddimInputs()
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
cl = TRUE, cv = TRUE)
message(paste0(date(), " ... Ending Dimensionality Reduction."))
redDim <- reducedDim(vals$counts, dimrednamesave)
if(input$dimRedPlotMethod == "scaterPCA"){
new_pca <- CreateDimReducObject(
embeddings = redDim,
assay = "RNA",
loadings = attr(redDim, "rotation"),
stdev = as.numeric(attr(redDim, "percentVar")),
key = "PC_")
}
else if(input$dimRedPlotMethod == "scanpyPCA"){
new_pca <- CreateDimReducObject(
embeddings = redDim,
assay = "RNA",
loadings = attr(redDim, "rotation"),
stdev = as.numeric(attr(redDim, "percentVar")),
key = "PC_")
}
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Component Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Elbow Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Heatmap Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "JackStraw Plot")
shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
if(input$computeElbowPlot
&& input$dimRedPlotMethod != "seuratICA"){
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Elbow Plot",
panel(
#heading = "Elbow Plot",
plotlyOutput(outputId = "plotDimRed_elbow")
)
),
select = TRUE
)
message(paste0(date(), " ... Generating Elbow Plot."))
if (input$dimRedPlotMethod == "seuratPCA"){
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = vals$counts, )
})
} else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]])
})
}
} else {
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = vals$counts,
externalReduction = new_pca)
})
} else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
externalReduction = new_pca)
})
}
}
}
if(input$computeHeatmapPlot){
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Heatmap Plot",
tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedHeatmap', function(x){
$('html').click();
});"),
panel(
fluidRow(
column(4, dropdown(
fluidRow(actionBttn(inputId = "closeDropDownDimRedHeatmap", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
selectizeInput(inputId = "picker_dimheatmap_components_dimRed",
label = "Select principal components to plot:",
choices = c(),
multiple = TRUE),
numericInput(
inputId = "slider_dimheatmap_dimRed",
label = "Number of columns for the plot: ",
min = 1,
max = 4,
value = 3
),
actionBttn(
inputId = "updateHeatmap_dimRed",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
),
inputId = "dropDownDimRedHeatmap",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(6, fluidRow(h6("Heatmaps of the top features correlated with each selected component"), align = "center"))
),
hr(),
br(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "plot_heatmap_dimRed"),
options = list(maxWidth = 700)
)
)
)
)
message(paste0(date(), " ... Generating Heatmaps."))
if (input$dimRedPlotMethod == "seuratPCA") {
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "pca"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "pca"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
else if(input$dimRedPlotMethod == "seuratICA"){
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "ica"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "ica"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
else{
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
externalReduction = new_pca
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[input$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
externalReduction = new_pca
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
compPrefix <- "PC"
if(input$dimRedPlotMethod == "seuratICA"){
compPrefix <- "IC"
}
compChoices <- rep(paste0(compPrefix, seq(input$dimRedNumberDims)))
updateSelectizeInput(
session = session,
inputId = "picker_dimheatmap_components_dimRed",
choices = compChoices,
selected = compChoices[seq(6)]
)
}
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Component Plot",
panel(
tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedComponentPlot', function(x){$('html').click();});"),
fluidRow(
column(
4,
dropdown(
fluidRow(
column(
12,
fluidRow(
actionBttn(inputId = "closeDropDownDimRedComponentPlot",
label = NULL, style = "simple",
color = "danger",
icon = icon("times"), size = "xs"),
align = "right"),
selectizeInput(
inputId = "plotDimRed_pca_selectRedDim",
label = "Select reducedDim:",
choices = reducedDimNames(vals$counts)
),
numericInput(inputId = "plotDimRed_pca_dimX",
label = "Select component for X-axis:",
value = 1),
numericInput(inputId = "plotDimRed_pca_dimY",
label = "Select component for Y-axis:",
value = 2),
actionBttn(
inputId = "updateRedDimPlot_pca",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
),
inputId = "dropDownDimRedComponentPlot",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(
6,
fluidRow(h6("Scatterplot of cells on selected components from a dimensionality reduction"), align = "center"))
),
hr(),
br(),
plotlyOutput(outputId = "plotDimRed_pca")
)
)
)
message(paste0(date(), " ... Plotting PCA/ICA."))
output$plotDimRed_pca <- renderPlotly({
plotly::ggplotly(
plotDimRed(
inSCE = vals$counts,
useReduction = dimrednamesave,
xAxisLabel = paste0(input$dimRedPlotMethod, "_1"),
yAxisLabel = paste0(input$dimRedPlotMethod, "_2"))
)
})
if(input$computeJackstrawPlot
&& input$dimRedPlotMethod != "seuratICA"){
appendTab(inputId = "dimRedPCAICA_plotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_jackstraw_dimRed"))
)
))
if (input$dimRedPlotMethod == "seuratPCA"){
message(paste0(date(), " ... Generating JackStraw Plot."))
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = vals$counts, dims = input$dimRedNumberDims)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = input$dimRedNumberDims)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]], dims = input$dimRedNumberDims)
})
}
}
else{
message(paste0(date(), " ... Generating JackStraw Plot."))
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims,
externalReduction = new_pca)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = vals$counts,
dims = input$dimRedNumberDims)
})
}
else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[input$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims,
externalReduction = new_pca)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
dims = input$dimRedNumberDims)
})
}
}
}
}
))
observeEvent(input$updateRedDimPlot_pca,{
req(vals$counts)
output$plotDimRed_pca <- renderPlotly({
isolate({
plotly::ggplotly(
plotDimRed(
inSCE = vals$counts,
useReduction = input$plotDimRed_pca_selectRedDim,
xDim = input$plotDimRed_pca_dimX,
yDim = input$plotDimRed_pca_dimY,
xAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimX),
yAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimY))
)
})
})
session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
})
observeEvent(input$closeDropDownDimRedComponentPlot, {
req(vals$counts)
session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
})
observeEvent(input$dimRedAssaySelect_tsneUmap, {
req(vals$counts)
if (!is.null(input$dimRedAssaySelect_tsneUmap)) {
if (input$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
shinyjs::disable("reductionMethodUMAPTSNEDimRed")
shinyjs::disable("logNorm_tsneUmap")
shinyjs::disable("hvg_tsneUmap")
shinyjs::disable("scale_tsneUmap")
shinyjs::disable("pca_tsneUmap")
updateCheckboxInput(session, "logNorm_tsneUmap", value = FALSE)
updateCheckboxInput(session, "scale_tsneUmap", value = FALSE)
updateCheckboxInput(session, "pca_tsneUmap", value = FALSE)
} else {
shinyjs::enable("reductionMethodUMAPTSNEDimRed")
shinyjs::enable("logNorm_tsneUmap")
shinyjs::enable("hvg_tsneUmap")
shinyjs::enable("scale_tsneUmap")
shinyjs::enable("pca_tsneUmap")
updateCheckboxInput(session, "scale_tsneUmap", value = TRUE)
updateCheckboxInput(session, "pca_tsneUmap", value = TRUE)
}
} else {
shinyjs::enable("reductionMethodUMAPTSNEDimRed")
}
})
observeEvent(input$runDimred_tsneUmap, withConsoleMsgRedirect(
msg = "Please wait while 2D embeddings are being created. See console log for progress.",
{
req(vals$counts)
message(date(), " ... Starting Dimensionality Reduction with: '",
input$dimRedPlotMethod_tsneUmap, "'.")
vals$runDimred$dimRedAssaySelect_tsneUmap <- input$dimRedAssaySelect_tsneUmap
if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
embedUseAssay <- NULL
embedUseRedDim <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseAltExp <- NULL
} else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% altExpNames(vals$counts)) {
embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseRedDim <- NULL
embedUseAltExp <- vals$runDimred$dimRedAssaySelect_tsneUmap
} else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% assayNames(vals$counts)) {
embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseRedDim <- NULL
embedUseAltExp <- NULL
}
if (input$dimRedNameInput_tsneUmap == "" ||
is.null(input$dimRedNameInput_tsneUmap)){
stop("Please enter a reducedDim name!")
}
if (input$dimRedNameInput_tsneUmap %in% names(reducedDims(vals$counts))){
stop("A reducedDim with name '", input$dimRedNameInput_tsneUmap,
"' is already stored in the object. Please specify a ",
"different name for this reducedDim.")
}
dimrednamesave <- gsub(" ", "_", input$dimRedNameInput_tsneUmap)
useFeatureSubset <- input$hvg_tsneUmap
if (input$hvg_tsneUmap == "None") {
useFeatureSubset <- NULL
}
if (input$dimRedPlotMethod_tsneUmap == "rTSNE"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "rTSNE",
logNorm = input$logNorm_tsneUmap,
useFeatureSubset = useFeatureSubset,
center = input$scale_tsneUmap,
scale = input$scale_tsneUmap,
pca = input$pca_tsneUmap,
initialDims = input$dimRedNumberDims_tsneUmap,
theta = input$thetaTSNE,
reducedDimName = dimrednamesave,
perplexity = input$perplexityTSNE,
nIterations = input$iterTSNE,
seed = input$seed__tsneUmap
)
} else if(input$dimRedPlotMethod_tsneUmap == "seuratTSNE"){
if (!is.null(embedUseRedDim)) {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratTSNE",
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
perplexity = input$perplexityTSNE,
seed = input$seed__tsneUmap
)
} else {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratTSNE",
useFeatureSubset = useFeatureSubset,
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
perplexity = input$perplexityTSNE,
useReduction = input$reductionMethodUMAPTSNEDimRed,
seed = input$seed__tsneUmap
)
}
} else if(input$dimRedPlotMethod_tsneUmap == "seuratUMAP"){
if (!is.null(embedUseRedDim)) {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratUMAP",
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
minDist = input$minDistUMAPDimRed,
nNeighbors = input$nNeighboursUMAPDimRed,
spread = input$spreadUMAPDimRed,
seed = input$seed__tsneUmap
)
} else {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratUMAP",
useFeatureSubset = useFeatureSubset,
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
minDist = input$minDistUMAPDimRed,
nNeighbors = input$nNeighboursUMAPDimRed,
spread = input$spreadUMAPDimRed,
useReduction = input$reductionMethodUMAPTSNEDimRed,
seed = input$seed__tsneUmap
)
}
} else if(input$dimRedPlotMethod_tsneUmap == "scaterUMAP") {
if (is.na(input$alphaUMAP)) {
stop("Learning rate (alpha) must be a numeric non-empty value!")
}
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scaterUMAP",
logNorm = input$logNorm_tsneUmap,
useFeatureSubset = useFeatureSubset,
scale = input$scale_tsneUmap,
pca = input$pca_tsneUmap,
initialDims = input$dimRedNumberDims_tsneUmap,
reducedDimName = dimrednamesave,
nNeighbors = input$neighborsUMAP,
nIterations = input$iterUMAP,
minDist = input$mindistUMAP,
alpha = input$alphaUMAP,
spread = input$spreadUMAP,
seed = input$seed__tsneUmap
)
} else if(input$dimRedPlotMethod_tsneUmap == "scanpyUMAP"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scanpyUMAP",
reducedDimName = dimrednamesave
)
} else if(input$dimRedPlotMethod_tsneUmap == "scanpyTSNE"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scanpyTSNE",
reducedDimName = dimrednamesave
)
}
updateReddimInputs()
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
cl = TRUE, cv = TRUE)
message(paste0(date(), " ... Ending Dimensionality Reduction."))
updateSelectizeInput(session, "selectRedDimPlot_tsneUmap",
choices = reducedDimNames(vals$counts),
selected = dimrednamesave,
server = TRUE)
message(paste0(date(), " ... Plotting 2D-Embedding."))
output$plotDimRed_tsneUmap <- renderPlotly({
isolate({
plotly::ggplotly(plotDimRed(
inSCE = vals$counts,
useReduction = dimrednamesave,
xAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_1"),
yAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_2")
))
})
})
}
))
observeEvent(input$updateRedDimPlot_tsneUmap,{
req(vals$counts)
output$plotDimRed_tsneUmap <- renderPlotly({
isolate({
plotly::ggplotly(plotDimRed(
inSCE = vals$counts,
useReduction = input$selectRedDimPlot_tsneUmap,
xAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_1"),
yAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_2")
))
})
})
session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
})
observeEvent(input$closeDropDownDimRedEmbedding,{
session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
})
#-----------------------------------------------------------------------------
# Page 3: Clustering ####
#-----------------------------------------------------------------------------
observeEvent(input$clustAlgo, {
if(input$clustAlgo %in% seq(7)){
# Scran SNN
updateTextInput(session, "clustName", value = "scran_snn_cluster")
enable("clustName")
} else if(input$clustAlgo %in% seq(8, 10)){
# K-Means
updateTextInput(session, "clustName", value = "kmeans_cluster")
enable("clustName")
} else if(input$clustAlgo %in% seq(11, 13)){
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Seurat", "_", algo, "_",
"Resolution", input$clustSeuratRes))
disable("clustName")
} else if(input$clustAlgo %in% seq(14, 15)){
algoList <- list('14' = "louvain",
'15' = "leiden")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Scanpy", "_", algo, "_", input$clustSeuratRes))
}
})
observeEvent(input$clustSeuratRes, {
if (input$clustAlgo %in% seq(11, 13)) {
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Seurat", "_", algo, "_",
"Resolution", input$clustSeuratRes))
disable("clustName")
}
})
clustResults <- reactiveValues(names = NULL)
observeEvent(input$clustRun, withConsoleMsgRedirect(
msg = "Please wait while clustering algorithm is being computed. See console log for progress.",
{
req(vals$counts)
if (input$clustName == "") {
stop("Cluster name should not be empty.")
}
saveClusterName = gsub(" ", "_", input$clustName)
if (input$clustAlgo %in% seq(7)) {
# Scran SNN
if (is.na(input$clustScranSNNK)) {
stop("K must be a numeric non-empty value!")
}
if (is.na(input$clustScranSNNd)) {
stop("Number of components must be a numeric non-empty value!")
}
algoList <- list('1' = "louvain", '2' = "leiden", '3' = "walktrap",
'4' = "infomap", '5' = "fastGreedy",
'6' = "labelProp", '7' = "leadingEigen")
algo <- algoList[[as.character(input$clustAlgo)]]
params = list(inSCE = vals$counts,
clusterName = saveClusterName,
k = input$clustScranSNNK,
weightType = input$clustScranSNNType,
algorithm = algo)
matType <- getTypeByMat(vals$counts, input$clustScranSNNMat)
if (is.null(matType)) {
return()
} else if (length(matType) == 1) {
if (matType == "assay") {
params$useAssay = input$clustScranSNNMat
params$nComp = input$clustScranSNNd
plotReddim <- NULL
} else if (matType == "reducedDim") {
params$useReducedDim = input$clustScranSNNMat
updateSelectInput(session, "clustVisReddim",
selected = input$clustScranSNNMat)
plotReddim <- input$clustScranSNNMat
} else if (matType == "altExp") {
params$useAltExp = input$clustScranSNNMat
params$altExpAssay = input$clustScranSNNMat
params$nComp = input$clustScranSNNd
plotReddim <- NULL
}
} else if (length(matType) == 2 &&
matType[1] == "reducedDim") {
# Using reddims saved in altExp
params$useAltExp = matType[2]
params$altExpRedDim = input$clustScranSNNMat
updateSelectInput(session, "clustVisReddim",
selected = input$clustScranSNNMat)
}
if (algo == 'leiden') {
params$resolution_parameter <- input$clustScranSNNLeidenReso
params$objective_function <- input$clusterScranSNNLeidenObjFunc
}
if (algo == "walktrap") {
params$steps <- input$clustScranSNNWalktrapStep
}
vals$counts <- do.call(runScranSNN, params)
} else if (input$clustAlgo %in% seq(8, 10)) {
# K-Means
if (input$clustKMeansReddim == "") {
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
if (is.na(input$clustKMeansN)) {
stop("Number of clusters/centers must be a numeric non-empty value!")
}
if (is.na(input$clustKMeansNIter)) {
stop("Max number of iterations must be a numeric non-empty value!")
}
if (is.na(input$clustKMeansNStart)) {
stop("Number of random sets must be a numeric non-empty value!")
}
algoList <- list('8' = "Hartigan-Wong",
'9' = "Lloyd", '10' = "MacQueen")
algo <- algoList[[as.character(input$clustAlgo)]]
vals$counts <- runKMeans(inSCE = vals$counts,
useReducedDim = input$clustKMeansReddim,
nCenters = input$clustKMeansN,
nIter = input$clustKMeansNIter,
nStart = input$clustKMeansNStart,
algorithm = algo,
clusterName = saveClusterName)
updateSelectInput(session, "clustVisReddim",
selected = input$clustKMeansReddim)
plotReddim <- input$clustKMeansReddim
} else if (input$clustAlgo %in% seq(11, 13)) {
# Seurat
if(input$clustSeuratReddim == ""){
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
if(is.na(input$clustSeuratDims)){
stop("Number of dimensions must be a numeric non-empty value!")
}
if(is.na(input$clustSeuratRes)){
stop("Resolution must be a numeric non-empty value!")
}
reddim <- reducedDim(vals$counts, input$clustSeuratReddim)
rownames(reddim) <- gsub("_", "-", rownames(reddim))
if ("percentVar" %in% names(attributes(reddim))) {
stdev <- as.numeric(attr(reddim, "percentVar"))
new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
stdev = stdev, key = "PC_")
} else {
new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
key = "PC_")
}
if (input$clustSeuratDims > ncol(reddim)) {
warning("More dimensions specified in dims than have been computed")
dims <- ncol(reddim)
} else {
dims <- input$clustSeuratDims
}
useAssay <- assayNames(vals$counts)[1]
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
useAssay = useAssay,
useReduction = "pca",
externalReduction = new_pca,
dims = dims,
algorithm = algo,
groupSingletons = input$clustSeuratGrpSgltn,
resolution = input$clustSeuratRes)
updateSelectInput(session, "clustVisReddim",
selected = input$clustSeuratReddim)
plotReddim <- input$clustSeuratReddim
}
else if (input$clustAlgo %in% seq(14, 15)){
algoList <- list('14' = "louvain",
'15' = "leiden")
algo <- algoList[[as.character(input$clustAlgo)]]
useAssay <- assayNames(vals$counts)[1] # change this
vals$counts <- runScanpyFindClusters(inSCE = vals$counts,
useAssay = useAssay,
algorithm = algo,
dims = input$clustScanpyDims,
resolution = input$clustScanpyRes,
nNeighbors = input$clustScanpyNeighbors,
niterations = input$clustScanpyIter,
use_weights = input$clustScanpyWeights,
cor_method = input$clustScanpyCorrMethod,
colDataName = input$clustName,
useReduction = input$clustKMeansReddim)
updateSelectInput(session, "clustVisReddim",
selected = input$clustKMeansReddim)
plotReddim <- input$clustKMeansReddim
}
updateColDataNames()
clustResults$names <- c(clustResults$names, saveClusterName)
updateSelectInput(session, "clustVisRes", choices = clustResults$names)
if (!is.null(plotReddim)) {
output$clustVisPlot <- renderPlotly({
isolate({
plotSCEDimReduceColData(inSCE = vals$counts,
colorBy = saveClusterName,
conditionClass = "factor",
reducedDimName = plotReddim,
labelClusters = TRUE,
dim1 = 1, dim2 = 2,
legendTitle = saveClusterName)
})
})
}
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-cl", parent = session,
de = TRUE, fm = TRUE, pa = TRUE, cv = TRUE, tj = TRUE)
}
))
observeEvent(input$closeDropDownClust, {
session$sendCustomMessage("close_dropDownClust", "")
})
observeEvent(input$clustPlot, {
req(vals$counts)
choice <- NULL
if (input$clustVisChoicesType == 1) {
# Use result
if (is.null(input$clustVisRes) ||
input$clustVisRes == "") {
shinyalert::shinyalert("Error!", "Select the clusters to plot",
type = "error")
}
choice <- input$clustVisRes
} else if (input$clustVisChoicesType == 2) {
# Use colData
if (is.null(input$clustVisCol) ||
input$clustVisCol == "") {
shinyalert::shinyalert("Error!", "Select the clusters to plot",
type = "error")
}
choice <- input$clustVisCol
}
if (is.null(input$clustVisReddim) || input$clustVisReddim == "") {
shinyalert::shinyalert("Error!",
"No reduction selected. Select one or run dimension reduction first",
type = "error")
}
if (!is.null(choice) && choice != "" &&
!is.null(input$clustVisReddim) && input$clustVisReddim != "") {
output$clustVisPlot <- renderPlotly({
isolate({
plotSCEDimReduceColData(inSCE = vals$counts,
colorBy = choice,
conditionClass = "factor",
reducedDimName = input$clustVisReddim,
labelClusters = TRUE,
dim1 = 1, dim2 = 2,
legendTitle = choice)
})
})
}
session$sendCustomMessage("close_dropDownClust", "")
})
#-----------------------------------------------------------------------------
# Trajectory Analysis####
#-----------------------------------------------------------------------------
updateTSCANUICollapse <- function() {
if (!is.null(vals$counts)) {
tscanResult <- metadata(vals$counts)$sctk$Traj$TSCAN$Pseudotime
if (is.null(tscanResult)) {
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
} else {
shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::enable(selector = "div[value='Plot feature expression on trajectory']")
}
} else {
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
}
}
###################################################
### Run STEP 1: TSCAN
###################################################
observeEvent(input$TSCANRun, withConsoleMsgRedirect(
msg = "Please wait while pseudotime is being computed. See console log for progress.",
{
req(vals$counts)
if (input$TSCANReddim == "") {
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
cluster <- input$TSCANclusterName
if (cluster == "Auto generate clusters") cluster <- NULL
vals$counts <- runTSCAN(inSCE = vals$counts,
useReducedDim = input$TSCANReddim,
cluster = cluster,
seed = handleEmptyInput(input$seed_TSCAN))
output$TSCANPlot <- renderPlot({
isolate({
plotTSCANResults(inSCE = vals$counts,
useReducedDim = input$TSCANReddim)
})
})
results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
terminalNodes <- colnames(results$pseudo)
terminalNodesList <- results$pathIndexList
updatePickerInput(session, "pathIndexx",
choices = terminalNodes,
choicesOpt = list(content=terminalNodesList),
selected = NULL)
clusterNamesList <- sort(unique(colData(vals$counts)$TSCAN_clusters))
updatePickerInput(session, "useClusterForPlotGene",
choices = clusterNamesList,
selected = NULL)
updatePickerInput(session, "plotTSCANDimReduceFeatures_useCluster",
choices = clusterNamesList)
updateSelectInput(session, "TSCANUseCluster",
choices = results$branchClusters)
updateCollapse(session = session, "TSCANUI",
style = list(`Calculate Pseudotime Values` = "success"))
updateTSCANUICollapse()
}
))
#plot results
observeEvent(input$TSCANPlot, {
req(vals$counts)
output$TSCANPlot <- renderPlot({
isolate({
plotTSCANResults(inSCE = vals$counts,
useReducedDim = input$TSCANVisRedDim)
})
})
updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim",
selected = input$TSCANVisRedDim)
updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim",
selected = input$TSCANVisRedDim)
session$sendCustomMessage("close_dropDownTSCAN", "")
})
###################################################
### Run STEP 2: Identify expressive genes
###################################################
observeEvent(input$pathIndexx, {
req(vals$counts)
results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
choices <- results$pathClusters[[input$pathIndexx]]
updatePickerInput(session, "discardCluster", choices = choices,
selected = NULL,
options = list(
`none-selected-text` = "No cluster discarded"
))
})
observeEvent(input$runTSCANDEG, withConsoleMsgRedirect(
msg = "Please wait while DE genes are being found for path. See console log for progress.",
{
req(vals$counts)
vals$counts <- runTSCANDEG(inSCE = vals$counts,
pathIndex = input$pathIndexx,
useAssay = input$TSCANassayselect,
discardCluster = input$discardCluster)
message(paste0(date(), " ... Expressive Genes Identified"))
message(paste0(date(), " ... Updating heatmap"))
output$heatmapPlot <- renderPlot({
isolate({
plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
pathIndex = input$pathIndexx)
})
})
message(paste0(date(), " ... Updating up-regulated genes"))
output$UpregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$pathIndexx,
direction = "increasing")
})
})
message(paste0(date(), " ... Updating down-regulated genes"))
output$DownregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$pathIndexx,
direction = "decreasing")
})
})
all.results <- getTSCANResults(vals$counts, analysisName = "DEG")
updateSelectInput(session, "tscanDEexpPathIndex",
choices = names(all.results),
selected = input$pathIndexx)
updateCollapse(
session = session, "TSCANUI",
style = list("Identify Genes Differentially Expressed For Path" = "success")
)
callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
de = TRUE, pa = TRUE)
}
))
observeEvent(input$tscanDEPlot, withConsoleMsgRedirect(
msg = "Please wait while TSCAN DE plots are being updated. See console log for progress",
{
req(vals$counts)
if (input$tscanDEFeatureDisplay == "Rownames (Default)") {
featureDisplay <- NULL
} else {
featureDisplay <- input$tscanDEFeatureDisplay
}
message(paste0(date(), " ... Updating heatmap"))
output$heatmapPlot <- renderPlot({
isolate({
plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
topN = input$tscanDEHMTopGenes,
featureDisplay = featureDisplay)
})
})
message(paste0(date(), " ... Updating up-regulated genes"))
output$UpregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
direction = "increasing",
topN = input$tscanDERegTopGenes,
featureDisplay = featureDisplay)
})
})
message(paste0(date(), " ... Updating down-regulated genes"))
output$DownregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
direction = "decreasing",
topN = input$tscanDERegTopGenes,
featureDisplay = featureDisplay)
})
})
session$sendCustomMessage("close_dropDownTscanDE", "")
}
))
###################################################
### Run STEP 3: Identify DE genes in specific cluster
###################################################
observeEvent(input$findDEGenes, withConsoleMsgRedirect(
msg = "Please wait while DE genes are being found for branched cluster. See console log for progress.",
{
req(vals$counts)
vals$counts <- runTSCANClusterDEAnalysis(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
useAssay = input$TSCANBranchAssaySelect,
fdrThreshold = input$fdrThreshold_TSCAN)
clusterAnalysisNames <- names(getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis"))
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$TSCANUseCluster)
pathChoices <- colnames(results$terminalNodes)
updateSelectInput(session, "plotTSCANClusterDEG_useCluster",
choices = clusterAnalysisNames,
selected = input$TSCANUseCluster)
#plot cluster deg by default
message(paste0(date(), " ... Plotting top DEG expression"))
output$tscanCLusterDEG <- renderPlot({
isolate({
plotTSCANClusterDEG(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
pathIndex = pathChoices[1],
topN = 4,
useReducedDim = input$TSCANVisRedDim)
})
})
#print list of DE genes by default
message(paste0(date(), " ... List of DE genes retrieved"))
df <- as.data.frame(results$DEgenes[[1]])
output$tscanCLusterDEGTable <- DT::renderDataTable({
isolate({
DT::datatable(
df,
options = list(scrollX = TRUE)
)
})
})
#plot cluster pseudo values by default
message(paste0(date(), " ... Plotting pseudotime of branches for cluster"))
output$tscanCLusterPeudo <- renderPlot({
isolate({
plotTSCANClusterPseudo(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
})
})
updateCollapse(session = session, "TSCANUI",
style = list("Identify Genes Differentially Expressed For Branched Cluster" = "success"))
callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
de = TRUE, pa = TRUE)
}
))
# Plot Top DEG expression on cluster
observeEvent(input$plotTSCANClusterDEG_useCluster, {
req(vals$counts)
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$plotTSCANClusterDEG_useCluster)
choices <- colnames(results$terminalNodes)
choicesOpt <- list(content = results$pathIndexList)
updatePickerInput(session, "plotTSCANClusterDEG_pathIndex",
choices = choices, choicesOpt = choicesOpt,
selected = NULL)
})
observeEvent(input$plotTSCANClusterDEG, withConsoleMsgRedirect(
msg = "Please wait while cluster DEG visualization is being updated. See console log for progress.",
{
req(vals$counts)
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$plotTSCANClusterDEG_useCluster)
req(results)
if (input$plotTSCANClusterDEG_featureDisplay == "Rownames (Default)") {
featureDisplay <- "rownames"
} else {
featureDisplay <- input$plotTSCANClusterDEG_featureDisplay
}
if (nrow(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]]) == 0) {
shinyalert(text = "No significant feature identified for the selected path.",
type = "warning")
}
message(date(), " ... Updating UMAP with feature expression")
plot <- plotTSCANClusterDEG(inSCE = vals$counts,
useCluster = input$plotTSCANClusterDEG_useCluster,
pathIndex = input$plotTSCANClusterDEG_pathIndex,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim,
topN = handleEmptyInput(input$plotTSCANClusterDEG_topN, type = "numeric"),
featureDisplay = featureDisplay)
output$tscanCLusterDEG <- renderPlot({
isolate({
plot
})
})
message(date(), " ... Updating DEG table")
df <- as.data.frame(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]])
output$tscanCLusterDEGTable <- DT::renderDataTable({
isolate({
DT::datatable(
df,
options = list(scrollX = TRUE)
)
})
})
message(date(), " ... Updating UMAP with pseudotime")
output$tscanCLusterPeudo <- renderPlot({
isolate({
plotTSCANClusterPseudo(inSCE = vals$counts,
useCluster = input$plotTSCANClusterDEG_useCluster,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
})
})
session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
})
)
###################################################
### Run STEP 4: Plot gene of interest
###################################################
observeEvent(input$plotTSCANDimReduceFeatures, withConsoleMsgRedirect(
msg = "Please wait when the expression of selected features are being plotted. See console log for progress.",
{
req(vals$counts)
if (is.null(input$plotTSCANDimReduceFeatures_features)) {
stop("Must select at least one feature.")
}
if (input$plotTSCANDimReduceFeatures_featureDisplay == "Rownames (Default)") {
featureDisplay <- "rownames"
} else {
featureDisplay <- input$plotTSCANDimReduceFeatures_featureDisplay
}
useCluster <- input$plotTSCANDimReduceFeatures_useCluster
output$TscanDimReduceFeatures <- renderPlot({
isolate({
plotTSCANDimReduceFeatures(inSCE = vals$counts,
features = input$plotTSCANDimReduceFeatures_features,
useReducedDim = input$plotTSCANDimReduceFeatures_useReducedDim,
useAssay = input$plotTSCANDimReduceFeatures_useAssay,
useCluster = useCluster,
featureDisplay = featureDisplay)
})
})
updateCollapse(session = session, "TSCANUI",
style = list("Plot feature expression on trajectory" = "success"))
}
))
##############################################################
observeEvent(input$closeDropDownTSCAN,{
session$sendCustomMessage("close_dropDownTSCAN", "")
})
observeEvent(input$closeDropDownTscanDE,{
session$sendCustomMessage("close_dropDownTscanDE", "")
})
observeEvent(input$closeDropDownTscanClusterDEG,{
session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
})
#-----------------------------------------------------------------------------
# Page 3.2: Celda ####
#-----------------------------------------------------------------------------
observeEvent(input$navbar, {
if(!is.null(vals$counts)){
if(input$navbar == "CeldaWorkflow"){
updateSelectInput(session, "celdaassayselect", choices = c(names(assays(vals$counts))))
}
}
})
modsplit <- reactiveVal()
cellsplit <- reactiveVal(NULL)
observeEvent(input$celdamodsplit, withConsoleMsgRedirect(
msg = "Please wait while recursive module split is being computed. See console log for progress.",
{
req(vals$counts)
removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Plot")
removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Difference Plot")
appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Rate of perplexity change",
panel(heading = "RPC Plot",
plotlyOutput(outputId = "plot_modsplit_perpdiff", height = "auto")
)
), select = TRUE)
appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Perplexity Plot",
panel(heading = "Perplexity Plot",
plotlyOutput(outputId = "plot_modsplit_perp", height = "auto")
)
))
if (input$celdafeatureselect == "None"){
vals$counts <- selectFeatures(vals$counts, minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect)
}else if(input$celdafeatureselect == "runSeuratFindHVG"){
vals$counts <- runSeuratNormalizeData(vals$counts, useAssay = input$celdaassayselect)
vals$counts <- runSeuratFindHVG(vals$counts, useAssay = "seuratNormData",
method = input$celdaseurathvgmethod, hvgNumber = input$celdafeaturenum)
g <- getTopHVG(vals$counts, method = input$celdaseurathvgmethod, n = input$celdafeaturenum)
altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
}else if(input$celdafeatureselect == "Scran_modelGeneVar"){
if (!("ScaterLogNormCounts" %in% names(assays(vals$counts)))){
vals$counts <- scater::logNormCounts(vals$counts, name = "ScaterLogNormCounts",
exprs_values = input$celdaassayselect)
}
vals$counts <- scranModelGeneVar(vals$counts, assayName = "ScaterLogNormCounts")
g <- getTopHVG(vals$counts, method = "modelGeneVar", n = input$celdafeaturenum)
altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
}
#counts(altExp(vals$counts)) <- as.matrix(counts(altExp(vals$counts)))
updateNumericInput(session, "celdaLselect", min = input$celdaLinit, max = input$celdaLmax, value = input$celdaLinit)
modsplit(recursiveSplitModule(vals$counts, useAssay = input$celdaassayselect, altExpName = "featureSubset", initialL = input$celdaLinit, maxL = input$celdaLmax))
output$plot_modsplit_perpdiff <- renderPlotly({plotRPC(modsplit(), sep = 10)})
output$plot_modsplit_perp <- renderPlotly({plotGridSearchPerplexity(modsplit())})
shinyjs::enable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
shinyjs::enable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
shinyjs::show(selector = ".celda_modsplit_plots")
message(paste0(date(), " ... Module Splitting Complete"))
shinyjs::show(id = "celdaLselect")
shinyjs::show(id = "celdaLbtn")
}
))
observeEvent(input$celdaLbtn, {
vals$counts <- subsetCeldaList(modsplit(), params = list(L = input$celdaLselect))
showNotification("Number of Feature Modules Selected.")
updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Feature Modules" = "success"))
shinyjs::enable(selector = "div[value='Identify Number of Cell Clusters']")
})
output$celdaKplots <- renderUI({
if (!is.null(vals$counts)){
if (!is.null(cellsplit())){
clusterlist <- runParams(cellsplit())$K
plot_output_list <- lapply(runParams(cellsplit())$K, function(i){
plotname <- paste0("Cluster", i)
tabPanel(title = sprintf("Cluster %s", i),
panel(heading = sprintf("Cluster %s", i),
plotlyOutput(plotname)
)
)
})
myTabs <- lapply(clusterlist, tabPanel)
do.call(tabsetPanel, plot_output_list)
}
}
})
observeEvent(input$celdacellsplit, withConsoleMsgRedirect(
msg = "Please wait while recursive split cell is being computed. See console log for progress.",
{
req(vals$counts)
cellsplit(recursiveSplitCell(vals$counts, useAssay = input$celdaassayselect, initialK = input$celdaKinit, maxK = input$celdaKmax,
yInit = celdaModules(vals$counts)))
temp_umap <- celdaUmap(vals$counts)
output$plot_cellsplit_perpdiff <- renderPlotly({plotRPC(cellsplit(), sep = 10)})
output$plot_cellsplit_perp <- renderPlotly({plotGridSearchPerplexity(cellsplit())})
for (i in runParams(cellsplit())$K){
local({
my_i <- i
plotname <- paste0("Cluster", my_i)
celdamod <- subsetCeldaList(cellsplit(), params = list(K = my_i))
output[[plotname]] <- renderPlotly(plotDimReduceCluster(celdamod,
dim1= reducedDim(altExp(temp_umap), "celda_UMAP")[, 1],
dim2 = reducedDim(altExp(temp_umap), "celda_UMAP")[, 2],
labelClusters = TRUE))
})
}
shinyjs::show(selector = ".celda_cellsplit_plots")
message(paste0(date(), " ... Cell Clustering Complete"))
updateNumericInput(session, "celdaKselect", min = input$celdaKinit, max = input$celdaKmax, value = input$celdaKinit)
shinyjs::show(id = "celdaKselect")
shinyjs::show(id = "celdaKbtn")
}
))
observeEvent(input$celdaKbtn, {
vals$counts <- subsetCeldaList(cellsplit(), params = list(K = input$celdaKselect))
showNotification("Number of Cell Clusters Selected.")
updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Cell Clusters" = "success"))
shinyjs::enable(
selector = "div[value='Visualization']")
updateNumericInput(session, "celdamodheatmapnum", min = 1, max = input$celdaLselect, value = 1)
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-celda", parent = session,
de = TRUE, pa = TRUE)
})
output$celdaheatmapplt <- renderPlot({plot(celdaHeatmap(vals$counts))})
output$celdaprobmapplt <- renderPlot({celdaProbabilityMap(vals$counts)})
observeEvent(input$CeldaUmap, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- celdaUmap(vals$counts,
useAssay = input$celdaassayselect,
maxCells = input$celdaUMAPmaxCells,
minClusterSize = input$celdaUMAPminClusterSize,
seed = input$celdaUMAPSeed,
minDist = input$celdaUMAPmindist,
spread = input$celdaUMAPspread,
nNeighbors = input$celdaUMAPnn)
output$celdaumapplot <- renderPlotly({plotDimReduceCluster(vals$counts, reducedDimName = "celda_UMAP", xlab = "UMAP_1",
ylab = "UMAP_2", labelClusters = TRUE)})
message(paste0(date(), " ... UMAP Complete"))
colData(vals$counts)$celda_clusters <- celdaClusters(vals$counts)
updateColDataNames()
shinyjs::enable("CeldaTsne")
}
))
observeEvent(input$CeldaTsne, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- celdaTsne(vals$counts,
useAssay = input$celdaassayselect,
maxCells = input$celdatSNEmaxCells,
minClusterSize = input$celdatSNEminClusterSize,
perplexity = input$celdatSNEPerplexity,
maxIter = input$celdatSNEmaxIter,
seed = input$celdatSNESeed)
output$celdatsneplot <- renderPlotly({
isolate({
plotDimReduceCluster(vals$counts, reducedDimName = "celda_tSNE",
xlab = "tSNE_1", ylab = "tSNE_2",
labelClusters = TRUE)
})
})
message(paste0(date(), " ... tSNE Complete"))
}
))
observeEvent(input$celdamodheatmapbtn,{
output$celdamodheatmapplt <- renderPlot({moduleHeatmap(vals$counts, topCells= input$celdamodheatmaptopcells, featureModule = input$celdamodheatmapnum)})
output$celdamodprobplt <- renderPlot({plotDimReduceModule(vals$counts, modules = input$celdamodheatmapnum, reducedDimName = "celda_UMAP")})
showNotification("Module heatmap complete.")
})
observe({
if(!is.null(vals$counts)){
#If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
shinyjs::enable(
selector = "div[value='Identify Number of Feature Modules']")
}else{
#If no data uploaded in data tab, disabled all tabs and plots.
#Disable tabs
shinyjs::disable(
selector = "div[value='Identify Number of Feature Modules']")
shinyjs::disable(
selector = "div[value='Identify Number of Cell Clusters']")
shinyjs::disable(
selector = "div[value='Visualization']")
#Disable plots inside Modsplit subtab
shinyjs::disable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
shinyjs::disable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
}
})
#-----------------------------------------------------------------------------
# Page 3.3: Cell Viewer ####
#-----------------------------------------------------------------------------
#-+-+-+-+-+-For Functional Panel collapse##############
shinyjs::onclick("cv_button1", shinyjs::toggle(id = "cv_collapse1",
anim = TRUE), add = TRUE)
shinyjs::onclick("cv_button2", shinyjs::toggle(id = "cv_collapse2",
anim = TRUE), add = TRUE)
shinyjs::onclick("cv_button3", shinyjs::toggle(id = "cv_collapse3",
anim = TRUE), add = TRUE)
shinyjs::addClass(id = "cv_button1", class = "btn-block")
shinyjs::addClass(id = "cv_button2", class = "btn-block")
shinyjs::addClass(id = "cv_button3", class = "btn-block")
colorbrewer_list <- rownames(RColorBrewer::brewer.pal.info)
color_table <- RColorBrewer::brewer.pal.info %>% data.frame()
color_seqdiv <- rownames(color_table[which(color_table$category == "div"
|color_table$category == "seq"),])
#-+-+-+-+-+-For Input Observe##############
observeEvent(input$navbar,{
if (input$navbar == "CellViewer"){
# is there an error or not
if (is.null(vals$counts)){
shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
}else{
gene_list <- rownames(vals$counts)
annotation_list <- names(colData(vals$counts))
annotation_list2 <- list()
for (i in 1:length(annotation_list)){
if(!all.is.numeric(vals$counts[[annotation_list[i]]])){
annotation_list2$Categorical <- c(annotation_list2$Categorical, annotation_list[i])
}else{
annotation_list2$Numeric <- c(annotation_list2$Numeric, annotation_list[i])
}
}
annotation_list <- annotation_list2
rm(annotation_list2)
updateSelectizeInput(session, "GeneSelect_Assays_Xaxis",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Xaxis",
choices = c(annotation_list))
updateSelectizeInput(session, "GeneSelect_Assays_Yaxis",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Yaxis",
choices = c(annotation_list))
updateSelectizeInput(session, "GeneSelect_Assays_Colorby",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Colorby",
choices = c(annotation_list))
updateSelectizeInput(session, "adjustgroupby", label = NULL, choices = c("None", annotation_list))
updateSelectizeInput(session,"adjustbrewer", label = "Color Palettes:",
choices = c("RdYlBu",color_seqdiv))
}
}
# if(input$navbar == "Feature Selection & Dimensionality Reduction"){
# gene_list <- rownames(vals$counts)
# updateSelectizeInput(session, "scatterFSGenes",
# choices = c(gene_list),
# server = TRUE)
# }
})
hide_TypeSelect <- reactiveVal("hide")
hide_bins <- reactiveVal()
observeEvent(input$viewertabs, {
if(!is.null(vals$counts)) {
if(!is.null(reducedDims(vals$counts))) {
approach_list <- names(reducedDims(vals$counts))
if (input$viewertabs != "Scatter Plot") {
updateSelectInput(session, "QuickAccess",
choices = c("Custom"))
shinyjs::delay(5,shinyjs::disable("QuickAccess"))
updateSelectInput(session, "TypeSelect_Xaxis",
choices = c("None", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Yaxis",
choices = c("Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Colorby",
selected = "Single Color")
updateSelectInput(session, "adjustgroupby",
selected = "None")
updatePrettyToggle(session, "checkColorbinning",
value = FALSE)
hide_TypeSelect("hide")
shinyjs::delay(5,shinyjs::disable("TypeSelect_Colorby"))
shinyjs::delay(5,shinyjs::disable("adjustgroupby"))
shinyjs::delay(5, shinyjs::disable("adjustlegendtitle"))
shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
} else {
updateSelectInput(session, "QuickAccess",
choices = c("", approach_list, "Custom"))
shinyjs::delay(5,shinyjs::enable("QuickAccess"))
updateSelectInput(session, "TypeSelect_Xaxis",
choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Yaxis",
choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Colorby",
selected = "Single Color")
updateSelectInput(session, "adjustgroupby",
selected = "None")
updatePrettyToggle(session, "checkColorbinning",
value = FALSE)
hide_TypeSelect("hide")
shinyjs::delay(5,shinyjs::enable("TypeSelect_Colorby"))
shinyjs::delay(5,shinyjs::enable("adjustgroupby"))
shinyjs::delay(5, shinyjs::enable("adjustlegendtitle"))
if (!is.null(input$adjustgridlines) &
isFALSE(input$adjustgridlines)) {
shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
}
}
if (input$viewertabs != "Bar Plot") {
shinyjs::delay(5, shinyjs::enable("adjustalpha"))
shinyjs::delay(5, shinyjs::enable("adjustsize"))
} else {
shinyjs::delay(5, shinyjs::disable("adjustalpha"))
shinyjs::delay(5, shinyjs::disable("adjustsize"))
}
}
}
})
observeEvent(input$adjustgridlines, {
req(vals$counts)
if (!is.null(input$adjustgridlines)) {
if (isTRUE(input$adjustgridlines)) {
shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
shinyjs::delay(5, shinyjs::disable("adjustaxissize"))
shinyjs::delay(5, shinyjs::disable("adjustaxislabelsize"))
} else {
if (input$viewertabs == "Scatter Plot") {
shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
}
shinyjs::delay(5, shinyjs::enable("adjustaxissize"))
shinyjs::delay(5, shinyjs::enable("adjustaxislabelsize"))
}
}
})
#-+-+-+-+-+-For Advanced Input Observe##############
###ApproachSelect to DimensionSelect X-Axis
observeEvent(input$ApproachSelect_Xaxis, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Xaxis) & len > 0){
Df <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Xaxis))
xs <- colnames(Df)
updateSelectInput(session, "ColumnSelect_Xaxis", choices = c(xs))
rm(Df)
}
}
})
###ApproachSelect to DimensionSelect Y-Axis
observeEvent(input$ApproachSelect_Yaxis, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Yaxis) & len > 0){
Df2 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Yaxis))
xs2 <- colnames(Df2)
xs2 <- sort(xs2, decreasing = TRUE)
updateSelectInput(session, "ColumnSelect_Yaxis", choices = c(xs2))
rm(Df2)
}
}
})
###ApproachSelect to DimensionSelect Colorby
observeEvent(input$ApproachSelect_Colorby, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Colorby) & len > 0){
Df3 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Colorby))
xs3 <- colnames(Df3)
prefix <- input$ApproachSelect_Colorby
suffix <- seq(1:length(xs3))
columns <- paste(prefix, suffix, sep = "_")
updateSelectInput(session, "ColumnSelect_Colorby", choices = c(columns))
rm(Df3)
}
}
})
#-+-+-+-+-+-Observe Color by###################################################
###Observe Radio Button Select Value Type
# input$AnnotationSelect_Colorby,
observe({
# All inputs to listen for
input$TypeSelect_Colorby
input$AnnotationSelect_Colorby
#Reduced Dimensions
input$ApproachSelect_Colorby
input$ColumnSelect_Colorby
#Expression Assay
input$AdvancedMethodSelect_Colorby
input$GeneSelect_Assays_Colorby
if(input$TypeSelect_Colorby == 'Cell Annotation'){
###If Cell Annotation is not numeric
if(!is.numeric(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("hide")
}else if(is.integer(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])
&length(levels(as.factor(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])))<=25){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("show")
}else{
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
}
} else if(input$TypeSelect_Colorby == 'Reduced Dimensions'){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
} else if(input$TypeSelect_Colorby == "Expression Assays"){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
} else {
# single color
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("hide")
}
})
observeEvent(input$SelectColorType,{
if(input$SelectColorType == "Categorical"){
hide_bins("hide")
}else{
hide_bins("show")
}
})
output$hide_typebtns <- renderText({
hide_TypeSelect()
})
outputOptions(output, "hide_typebtns", suspendWhenHidden = FALSE)
output$hide_bins <- renderText({
hide_bins()
})
outputOptions(output, "hide_bins", suspendWhenHidden = FALSE)
numColors <- NULL
colorLabels <- NULL
### Observe input changes that should trigger categorical color generator
observeEvent(c(input$SelectColorType, input$TypeSelect_Colorby, input$AnnotationSelect_Colorby, input$colorTheme), {
if (input$TypeSelect_Colorby == "Single Color") {
shinyjs::hide("categoricalColorConditional")
shinyjs::hide("continuousColorConditional")
}
if (input$SelectColorType == "Categorical" && input$TypeSelect_Colorby != "Single Color") {
if(input$TypeSelect_Colorby == "Cell Annotation") {
req(vals$counts, input$AnnotationSelect_Colorby)
labels = sort(unique(SingleCellExperiment::colData(vals$counts)[, input$AnnotationSelect_Colorby]))
if (length(labels) <=25 ) {
colorLabels <<- labels
numColors <<- length(labels)
defaultColors <- discreteColorPalette(numColors, input$colorTheme)
output$categoricalColorUI <- renderUI({
lapply(1:numColors, function(i){
colourInput(inputId=paste0(i, "_color"), label=labels[i], value=defaultColors[i], showColour="background")
})
})
shinyjs::show("categoricalColorConditional")
shinyjs::hide("continuousColorConditional")
}
}
} else if (input$SelectColorType == "Continuous" && input$TypeSelect_Colorby != "Single Color") {
shinyjs::hide("categoricalColorConditional")
shinyjs::show("continuousColorConditional")
}
})
testvar <- reactive({
event.data <- event_data("plotly_selected")
#curveNumber pointNumber x y
#pointnumbers <- event.data[["pointNumber"]]
})
observeEvent(input$subsetCelda, {
if(!is.null(vals$counts)){
copy <- vals$counts
if(!("featureSubset" %in% altExpNames(copy))){
copy <- selectFeatures(copy)
}
selection <- event_data("plotly_selected")
copy2 <- subsetSCECols(copy, index = testvar()$pointNumber)
clustersog <- celdaClusters(copy)[testvar()$pointNumber]
#copy2 <- subsetSCECols(copy, index = testvar())
maxclust <- max(celdaClusters(copy))
#celdaClusters(copy) <- 0
#celdaClusters(copy) <- celdaClusters(copy2)
#celdaClusters(copy)[testvar()$pointNumber] <- maxclust + 1
#celdaClusters(copy)[colnames(copy) %in% colnames(copy2)] <- clustersog
#celdaClusters(copy)[testvar()] <- 2
saveRDS(copy, "celdasubsettest.RDS")
}
#subset <- testvar()
})
observeEvent(input$subsetCelda2, {
if(!is.null(vals$counts)){
copy <- vals$counts
if(!("featureSubset" %in% altExpNames(copy))){
copy <- selectFeatures(copy)
}
selection <- event_data("plotly_selected")
#copy2 <- subsetSCECols(copy, index = testvar())
#maxclust <- max(celdaClusters(copy))
celdaClusters(copy) <- 1
celdaClusters(copy)[testvar()$pointNumber] <- 2
saveRDS(copy, "celdasubsettest2.RDS")
}
#subset <- testvar()
})
output$testprint <- renderText({as.character(testvar())})
#-+-+-+-+-+-cellviewer prepare step1: choose data. (next steps included)###########################################################
#cellviewer <- eventReactive(input$runCellViewer,{
observeEvent(input$runCellViewer,{
colors <- c()
if (!is.null(numColors) && input$SelectColorType == 'Categorical') {
for (i in 1: numColors) {
colors[i] <- input[[ paste0(i,"_color")]]
}
names(colors) = colorLabels
}
#-+-+-+-+-+-cellviewer prepare3 : prepare Axis Label Name#####################
###Xaxis label name
if (!is.null(input$adjustxlab) &
input$adjustxlab != "") {
xname <- input$adjustxlab
} else {
if (input$QuickAccess != "Custom" &
input$QuickAccess != "") {
# reddim selected
xname <- paste0(input$QuickAccess, 1)
} else if (input$TypeSelect_Xaxis == 'Reduced Dimensions') {
xname <- paste0(input$ApproachSelect_Xaxis, "_",
substr(input$ColumnSelect_Xaxis,
str_length(input$ColumnSelect_Xaxis),
str_length(input$ColumnSelect_Xaxis)))
} else if (input$TypeSelect_Xaxis == 'Expression Assays') {
xname <- input$GeneSelect_Assays_Xaxis
} else if (input$TypeSelect_Xaxis == "Cell Annotation") {
xname <- input$AnnotationSelect_Xaxis
} else {
xname <- ""
}
}
xname <- gsub("-", "_", xname)
###Yaxis label name
if (!is.null(input$adjustylab) &
input$adjustylab != "") {
yname <- input$adjustylab
} else {
if (input$QuickAccess != "Custom" &
input$QuickAccess != "") {
# reddim selected
yname <- paste0(input$QuickAccess, 2)
} else if (input$TypeSelect_Yaxis == 'Reduced Dimensions') {
yname <- paste0(input$ApproachSelect_Yaxis, "_",
substr(input$ColumnSelect_Yaxis,
str_length(input$ColumnSelect_Yaxis),
str_length(input$ColumnSelect_Yaxis)))
} else if (input$TypeSelect_Yaxis == 'Expression Assays') {
yname <- input$GeneSelect_Assays_Yaxis
} else {
yname <- input$AnnotationSelect_Yaxis
}
}
yname <- gsub("-", "_", yname)
###Legend name
if (input$TypeSelect_Colorby != 'Pick a Color') {
if (input$TypeSelect_Colorby == 'Reduced Dimensions' && input$adjustlegendtitle == "") {
legendname <- paste0(input$ApproachSelect_Colorby,"_",substr(input$ColumnSelect_Colorby,
str_length(input$ColumnSelect_Colorby),str_length(input$ColumnSelect_Colorby)))
} else if (input$TypeSelect_Colorby == 'Expression Assays' && input$adjustlegendtitle == "") {
legendname <- input$GeneSelect_Assays_Colorby
} else if (input$adjustlegendtitle == "") {
legendname <- input$AnnotationSelect_Colorby
} else {
legendname <- input$adjustlegendtitle
}
}
legendname <- gsub("-", "_", legendname)
#-+-+-+-+-+-cellviewer prepare4 : choose group by and create plotly function###################
pltVars <- list()
if(input$viewertabs == "Violin/Box Plot" || input$viewertabs == "Bar Plot"){
if(input$TypeSelect_Xaxis == "None"){
pltVars$groupby <- NULL
}else if(input$TypeSelect_Xaxis == "Expression Assays"){
pltVars$groupby <- input$GeneSelect_Assays_Xaxis
}else if(input$TypeSelect_Xaxis == "Cell Annotation"){
pltVars$groupby <- input$AnnotationSelect_Xaxis
}
}else if(input$adjustgroupby != "None"){
pltVars$groupby <- input$adjustgroupby
}else{
pltVars$groupby <- NULL
}
if (input$checkColorbinning == TRUE && input$SelectColorType == "Continuous"){
pltVars$bin <- input$adjustColorbinning
}else{
pltVars$bin <- NULL
}
if (input$SelectColorType == "Categorical"){
pltVars$class <- "factor"
}else{
pltVars$class <- "numeric"
}
if(input$adjustgridlines == TRUE){
pltVars$defTheme <- FALSE
}else{
pltVars$defTheme <- TRUE
}
if(input$viewertabs == "Scatter Plot"){
#### Prepare Custom plotting matrix axis ####
if (input$QuickAccess == "Custom") {
# X Axis
message("CellViewer: Custom plotting mode, making up the axis")
if (input$TypeSelect_Xaxis == "Expression Assays") {
message("X axis: Using expression of ", input$GeneSelect_Assays_Xaxis,
" from ", input$AdvancedMethodSelect_Xaxis)
xvec <- expData(vals$counts, input$AdvancedMethodSelect_Xaxis)[input$GeneSelect_Assays_Xaxis,]
} else if (input$TypeSelect_Xaxis == "Reduced Dimensions") {
message("X axis: Using dimension reduction ", input$ColumnSelect_Xaxis,
" from ", input$ApproachSelect_Xaxis)
xvec <- reducedDim(vals$counts, input$ApproachSelect_Xaxis)[,input$ColumnSelect_Xaxis]
} else if (input$TypeSelect_Xaxis == 'Cell Annotation') {
message("X axis: Using cell annotation ",
input$AnnotationSelect_Xaxis)
xvec <- vals$counts[[input$AnnotationSelect_Xaxis]]
}
# Y Axis
if (input$TypeSelect_Yaxis == "Expression Assays") {
message("Y axis: Using expression of ", input$GeneSelect_Assays_Yaxis,
" from ", input$AdvancedMethodSelect_Yaxis)
yvec <- expData(vals$counts, input$AdvancedMethodSelect_Yaxis)[input$GeneSelect_Assays_Yaxis,]
} else if (input$TypeSelect_Yaxis == "Reduced Dimensions") {
message("Y axis: Using dimension reduction ", input$ColumnSelect_Yaxis,
" from ", input$ApproachSelect_Yaxis)
yvec <- reducedDim(vals$counts, input$ApproachSelect_Yaxis)[,input$ColumnSelect_Yaxis]
} else if (input$TypeSelect_Yaxis == 'Cell Annotation') {
message("Y axis: Using cell annotation ",
input$AnnotationSelect_Yaxis)
yvec <- vals$counts[[input$AnnotationSelect_Yaxis]]
}
# Merge and insert to reducedDim(sce, "Custom")
customMat <- matrix(c(xvec, yvec), nrow = length(xvec))
colnames(customMat) <- c(xname, yname)
rownames(customMat) <- names(xvec)
reducedDim(vals$counts, "Custom") <- customMat
}
if(input$TypeSelect_Colorby == "Single Color"){
a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess,
xlab = xname, ylab = yname, title = input$adjusttitle, groupBy = pltVars$groupby,
transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize,
conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Colorby == "Expression Assays"){
a <- plotSCEDimReduceFeatures(vals$counts, feature = input$GeneSelect_Assays_Colorby,
reducedDimName = input$QuickAccess, useAssay = input$AdvancedMethodSelect_Colorby,
xlab = xname, ylab = yname, legendTitle = legendname, title = input$adjusttitle,
groupBy = pltVars$groupby, bin = pltVars$bin, transparency = input$adjustalpha,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, legendSize = input$adjustlegendsize,
legendTitleSize = input$adjustlegendtitlesize)
} else if (input$TypeSelect_Colorby == "Cell Annotation") {
a <- plotSCEDimReduceColData(vals$counts,reducedDimName = input$QuickAccess,xlab = xname,ylab = yname,
colorBy = input$AnnotationSelect_Colorby,groupBy = pltVars$groupby,legendTitle = legendname,
title = input$adjusttitle,bin = pltVars$bin,transparency = input$adjustalpha,colorScale = colors,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor, dotSize = input$adjustsize,
combinePlot = "none",axisSize = input$adjustaxissize,axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
}else if(input$TypeSelect_Colorby == "Reduced Dimensions"){
a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess, slot = "reducedDims",
annotation = input$ColumnSelect_Colorby, transparency = input$adjustalpha,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
groupBy = pltVars$groupby, title = input$adjusttitle, legendTitle = legendname,
xlab = xname, ylab = yname, dotSize = input$adjustsize, bin = pltVars$bin,
combinePlot = "none", axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize)
}
}else if(input$viewertabs == "Bar Plot"){
if(input$TypeSelect_Yaxis == "Expression Assays"){
a <- plotSCEBarAssayData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
useAssay = input$AdvancedMethodSelect_Yaxis, groupBy = pltVars$groupby,
feature = input$GeneSelect_Assays_Yaxis,
combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Yaxis == "Cell Annotation"){
a <- plotSCEBarColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
coldata = input$AnnotationSelect_Yaxis, groupBy = pltVars$groupby,
combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
defaultTheme = as.logical(pltVars$defTheme))
}
}else if(input$viewertabs == "Violin/Box Plot"){
if(isTRUE(input$vlnboxcheck)){
vln <- TRUE
bx <- FALSE
}else if(isFALSE(input$vlnboxcheck)){
vln <- FALSE
bx <- TRUE
}
if(input$TypeSelect_Yaxis == "Expression Assays"){
a <- plotSCEViolinAssayData(vals$counts, violin = vln, box = bx, xlab = xname, ylab = yname,
useAssay = input$AdvancedMethodSelect_Yaxis, title = input$adjusttitle,
feature = input$GeneSelect_Assays_Yaxis, groupBy = pltVars$groupby,
transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Yaxis == "Cell Annotation"){
a <- plotSCEViolinColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
coldata = input$AnnotationSelect_Yaxis, violin = vln, box = bx,
groupBy = pltVars$groupby, transparency = input$adjustalpha,
dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
}
}
if (input$TypeSelect_Colorby == "Single Color"){
a$layers[[1]]$aes_params$colour <- input$Col
}
if (isTRUE(input$adjustgridlines)){
a <- a + ggplot2::theme_bw()
}
a <- plotly::ggplotly(a)
output$scatter <- renderPlotly({
plotly::subplot(plotlist = a, titleX = TRUE, titleY = TRUE)
})
})
#-----------------------------------------------------------------------------
# Page 3.4: Heatmap ####
#-----------------------------------------------------------------------------
hmTemp <- reactiveValues(
sce = NULL,
cellIndex = NULL,
geneIndex = NULL,
colDataName = NULL,
rowDataName = NULL,
colSplitBy = NULL,
rowSplitBy = NULL,
cellTableCol = NULL,
geneTableCol = NULL,
colColorPresets = list(),
rowColorPresets = list()
)
observeEvent(vals$counts, {
if(!is.null(vals$counts)){
hmTemp$sce <- vals$counts
}
})
# Heatmap: Import Analysis ####
observeEvent(input$hmImportRun, {
if(!is.null(vals$counts)){
if(!is.null(input$hmImport) &&
input$hmImport == "Differential Expression"){
if(!is.null(input$hmImpDEG)){
result <- metadata(vals$counts)$diffExp[[input$hmImpDEG]]
useAssay <- result$useAssay
updateSelectInput(session, "hmAssay", selected = useAssay)
method <- result$method
# Cell side
addColData <- data.frame(row.names = colnames(vals$counts))
idx <- rep(NA, ncol(vals$counts))
idx[result$select$ix1] <- result$groupNames[1]
idx[result$select$ix2] <- result$groupNames[2]
hmTemp$cellIndex <- which(!is.na(idx))
conditionColName <- paste(method, input$hmImpDEG, "condition",
sep = '_')
addColData[[conditionColName]] <- factor(idx,
levels = result$groupNames)
colData(hmTemp$sce) <- cbind(colData(hmTemp$sce), addColData)
hmTemp$cellTableCol <- conditionColName
hmTemp$colDataName <- conditionColName
hmTemp$colSplitBy <- conditionColName
hmTemp$colColorPresets[[conditionColName]] <- c('red', 'cyan',
'white')
names(hmTemp$colColorPresets[[conditionColName]]) <-
c(result$groupNames, "NA")
# Gene side
addRowData <- data.frame(row.names = rownames(vals$counts))
deg <- result$result
deg <- deg[stats::complete.cases(deg),]
logFCColName <- paste(method, input$hmImpDEG, "Log2FC",
sep = '_')
FDRColName <- paste(method, input$hmImpDEG, "FDR",
sep = '_')
addRowData[deg$Gene, logFCColName] <- deg$Log2_FC
addRowData[deg$Gene, FDRColName] <- deg$FDR
regColName <- paste(method, input$hmImpDEG, "regulation",
sep = '_')
degUp <- deg[deg$Log2_FC > 0,]
degDown <- deg[deg$Log2_FC < 0,]
addRowData[degUp$Gene, regColName] <- "up"
addRowData[degDown$Gene, regColName] <- "down"
addRowData[[regColName]] <- factor(addRowData[[regColName]],
levels = c('up', 'down'))
rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
hmTemp$geneTableCol <- c(regColName, logFCColName, FDRColName)
hmTemp$geneIndex <- which(rownames(vals$counts) %in% deg$Gene)
hmTemp$rowDataName <- regColName
hmTemp$rowSplitBy <- regColName
hmTemp$rowColorPresets[[regColName]] <- c('red', 'cyan', 'white')
names(hmTemp$rowColorPresets[[regColName]]) <- c('up', 'down', 'NA')
}
} else if (!is.null(input$hmImport) &&
input$hmImport == "Find Marker"){
markerTable <- metadata(vals$counts)$findMarker
if(!is.null(markerTable) &&
dim(markerTable)[1] > 0){
markerTable <- markerTable[stats::complete.cases(markerTable),]
# Cell side
cluster <- colnames(markerTable)[5]
hmTemp$cellIndex <- seq_len(ncol(hmTemp$sce))
hmTemp$colDataName <- cluster
hmTemp$cellTableCol <- cluster
hmTemp$colSplitBy <- cluster
# Gene side
dup.gene <- unique(markerTable$Gene[duplicated(markerTable$Gene)])
for(g in dup.gene){
deg.gix <- markerTable$Gene == g
deg.gtable <- markerTable[deg.gix,]
toKeep <- which.max(deg.gtable$Log2_FC)
toRemove <- which(deg.gix)[-toKeep]
markerTable <- markerTable[-toRemove,]
}
hmTemp$geneIndex <- which(rownames(vals$counts) %in% markerTable$Gene)
addRowData <- data.frame(row.names = rownames(vals$counts))
addRowData[markerTable$Gene, "Marker_for_Cluster"] <- markerTable[,5]
addRowData[markerTable$Gene, "findMarker_Log2FC"] <-
markerTable$Log2_FC
addRowData[markerTable$Gene, "findMarker_FDR"] <- markerTable$FDR
rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
hmTemp$geneTableCol <- c("Marker_for_Cluster",
"findMarker_Log2FC",
"findMarker_FDR")
hmTemp$rowDataName <- "Marker_for_Cluster"
hmTemp$rowSplitBy <- "Marker_for_Cluster"
hmTemp$rowColorPresets$Marker_for_Cluster <-
hmAnnAllColors$col[[cluster]]
}
}
}
})
# Heatmap: Subsetting Cells####
output$hmCellColUI <- renderUI({
if(!is.null(vals$counts)){
selectInput(
'hmCellCol',
"Columns to display",
names(colData(hmTemp$sce)), multiple = TRUE, width = '550px',
selected = hmTemp$cellTableCol)
}
})
output$hmCellColTable <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- as.data.frame(colData(hmTemp$sce))
rowNameCol <- data.frame(Row_Names = colnames(vals$counts))
df <- cbind(rowNameCol, df)
rownames(df) <- NULL
DT::datatable(
df,
filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
)
}
}, server = TRUE)
hmCellColTable_proxy <- DT::dataTableProxy("hmCellColTable")
observeEvent(input$hmCellCol, {
colNames <- c('Row_Names', names(colData(hmTemp$sce)))
showIdx <- which(colNames %in% input$hmCellCol)
showIdx <- c(1, showIdx)
DT::showCols(hmCellColTable_proxy, showIdx, reset = TRUE)
})
observeEvent(input$hmCellColTable_state, {
DT::selectRows(hmCellColTable_proxy, hmTemp$cellIndex)
})
observeEvent(input$hmCellColTable_rows_selected, {
hmTemp$cellIndex <- input$hmCellColTable_rows_selected
})
observeEvent(input$hmCellColTable_addAll, {
DT::selectRows(hmCellColTable_proxy,
sort(unique(c(input$hmCellColTable_rows_selected,
input$hmCellColTable_rows_all))))
hmTemp$cellIndex <- sort(unique(c(input$hmCellColTable_rows_selected,
input$hmCellColTable_rows_all)))
})
observeEvent(input$hmCellColTable_clear, {
DT::selectRows(hmCellColTable_proxy, NULL)
hmTemp$cellIndex <- NULL
})
observeEvent(input$hmCellCol, {
hmTemp$cellTableCol <- input$hmCellCol
})
output$hmCellNEnteredUI <- renderUI({
inputList <- str_trim(scan(text = input$hmCellText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
nInput <- length(uniqInput)
if(!is.null(vals$counts) && nInput > 0){
if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmCellTextBy
}
matched <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
by = BY, exactMatch = input$hmCellTextEM,
firstMatch = input$hmCellTextFM)
nMatched <- length(matched)
} else {
nMatched <- 0
}
p(paste0(nInput, " unique input, ", nMatched, "matched."))
})
observeEvent(input$hmCellAddFromText, {
if(!is.null(vals$counts)){
inputList <- str_trim(scan(text = input$hmCellText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
if(length(uniqInput) > 0){
if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmCellTextBy
}
newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
by = BY, exactMatch = input$hmCellTextEM,
firstMatch = input$hmCellTextFM)
DT::selectRows(hmCellColTable_proxy,
sort(unique(c(input$hmCellColTable_rows_selected,
newIdx))))
}
}
})
output$hmCellSumUI <- renderUI({
nCell <- length(hmTemp$cellIndex)
if(nCell == 0){
p("No cells selected, going to use them all", style = 'margin-top: 5px;')
} else {
p(paste0("Totally ", nCell, " cells selected."),
style = 'margin-top: 5px;')
}
})
# Heatmap: Subsetting Genes ####
output$hmGeneColUI <- renderUI({
if(!is.null(vals$counts)){
selectInput(
'hmGeneCol',
"Columns to display",
names(rowData(hmTemp$sce)), multiple = TRUE, width = '550px',
selected = hmTemp$geneTableCol)
}
})
output$hmGeneColTable <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- as.data.frame(rowData(hmTemp$sce))
rowNameCol <- data.frame(Row_Names = rownames(vals$counts))
df <- cbind(rowNameCol, df)
rownames(df) <- NULL
DT::datatable(
df,
filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
)
}
}, server = TRUE)
hmGeneColTable_proxy <- DT::dataTableProxy("hmGeneColTable")
observeEvent(input$hmGeneCol, {
colNames <- c('Row_Names', names(rowData(hmTemp$sce)))
showIdx <- which(colNames %in% input$hmGeneCol)
showIdx <- c(1, showIdx)
DT::showCols(hmGeneColTable_proxy, showIdx, reset = TRUE)
})
observeEvent(input$hmGeneColTable_state, {
DT::selectRows(hmGeneColTable_proxy, hmTemp$geneIndex)
})
observeEvent(input$hmGeneColTable_rows_selected, {
hmTemp$geneIndex <- input$hmGeneColTable_rows_selected
})
observeEvent(input$hmGeneColTable_addAll, {
DT::selectRows(hmGeneColTable_proxy,
sort(unique(c(input$hmGeneColTable_rows_selected,
input$hmGeneColTable_rows_all))))
hmTemp$geneIndex <- sort(unique(c(input$hmGeneColTable_rows_selected,
input$hmGeneColTable_rows_all)))
})
observeEvent(input$hmGeneColTable_clear, {
DT::selectRows(hmGeneColTable_proxy, NULL)
hmTemp$geneIndex <- NULL
})
observeEvent(input$hmGeneCol, {
hmTemp$geneTableCol <- input$hmGeneCol
})
output$hmGeneNEnteredUI <- renderUI({
inputList <- str_trim(scan(text = input$hmGeneText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
nInput <- length(uniqInput)
if(!is.null(vals$counts) && nInput > 0){
if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmGeneTextBy
}
matched <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
by = BY, exactMatch = input$hmGeneTextEM,
firstMatch = input$hmGeneTextFM)
nMatched <- length(matched)
} else {
nMatched <- 0
}
p(paste0(nInput, " unique input, ", nMatched, "matched."))
})
observeEvent(input$hmGeneAddFromText, {
if(!is.null(vals$counts)){
inputList <- str_trim(scan(text = input$hmGeneText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
if(length(uniqInput) > 0){
if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmGeneTextBy
}
newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
by = BY, exactMatch = input$hmGeneTextEM,
firstMatch = input$hmGeneTextFM)
DT::selectRows(hmGeneColTable_proxy,
sort(unique(c(input$hmGeneColTable_rows_selected,
newIdx))))
}
}
})
output$hmGeneSumUI <- renderUI({
nGene <- length(hmTemp$geneIndex)
if(nGene == 0){
p("No features selected, going to use them all",
style = 'margin-top: 5px;')
} else {
p(paste0("Totally ", nGene, " features selected."),
style = 'margin-top: 5px;')
}
})
# Heatmap: Annotation color assignment ####
output$hmCellAnnUI <- renderUI({
if(!is.null(vals$counts)){
classes <- names(colData(hmTemp$sce))
selectInput('hmCellAnn', 'Add cell annotation', classes,
multiple = TRUE, selected = hmTemp$colDataName)
}
})
output$hmGeneAnnUI <- renderUI({
if(!is.null(vals$counts)){
classes <- names(rowData(hmTemp$sce))
selectInput('hmGeneAnn', 'Add feature annotation', classes,
multiple = TRUE, selected = hmTemp$rowDataName)
}
})
observeEvent(input$hmCellAnn, {
hmTemp$colDataName <- input$hmCellAnn
})
observeEvent(input$hmGeneAnn, {
hmTemp$rowDataName <- input$hmGeneAnn
})
hmAnnAllColors <- reactiveValues(
col = NULL,
row = NULL
)
generateAnnColAssUI <- function(colname, axis){
if(axis == "row"){
data <- as.vector(rowData(hmTemp$sce)[[colname]])
} else if(axis == 'col'){
data <- as.vector(colData(hmTemp$sce)[[colname]])
}
nUniq <- length(as.vector(unique(data[!is.na(data)])))
if(colname %in% names(hmTemp[[paste0(axis, "ColorPresets")]])){
cats = names(hmTemp[[paste0(axis, "ColorPresets")]][[colname]])
fluidRow(style = "padding-left:20px;",
h4(colname),
lapply(seq_along(cats), function(i) {
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = cats[i],
value = hmTemp[[paste0(axis, "ColorPresets")]][[colname]][[cats[i]]]
)
)
})
)
} else if(nUniq > 12){
if(is.numeric(data)){
fluidRow(style = "padding-left:20px;",
h4(colname),
p(paste0("Numeric annotation with ", nUniq, " unique values detected. Please choose the type of legend.")),
radioButtons(
inputId = paste0('hm', axis, colname, 'type'),
label = NULL,
choices = c('Categorical', 'Continuous'),
inline = TRUE
),
conditionalPanel(
condition = paste0("input.hm", axis, colname, "type == 'Categorical'"),
p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
),
conditionalPanel(
condition = paste0("input.hm", axis, colname, "type == 'Continuous'"),
p("We generate a gradient color legend for continuous annotation value"),
column(
width = 6,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, 'High'),
label = 'High Value'
)
),
column(
width = 6,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, 'Low'),
label = 'Low Value'
)
)
),
)
} else {
fluidRow(style = "padding-left:20px;", h4(colname),
p(paste0("Totally ", nUniq, " unique values in this class of annotation, which is too many to provide manual selection. Coloring will be provided by default."))
)
}
} else if(nUniq >= 1 && nUniq <= 12){
cats <- as.character(unique(data))
fluidRow(style = "padding-left:20px;",
h4(colname),
lapply(seq_along(cats), function(i) {
if(!is.na(cats[i])){
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = cats[i],
value = hmAnnAllColors[[axis]][[colname]][[cats[i]]]
)
)
} else {
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = "NA",
value = #FFFFFF
)
)
}
})
)
} else {
fluidRow(style = "padding-left:20px;",
h4(colname),
p("No effective category found for the class.")
)
}
}
observeEvent(input$hmCellAnn, {
if(!is.null(input$hmCellAnn)){
output$hmCellAnnAssUI <- renderUI({
panel(
lapply(input$hmCellAnn, generateAnnColAssUI, axis = 'col')
)
})
}
})
observeEvent(input$hmGeneAnn, {
if(!is.null(input$hmGeneAnn)){
output$hmGeneAnnAssUI <- renderUI({
panel(
lapply(input$hmGeneAnn, generateAnnColAssUI, axis = 'row')
)
})
}
})
observe({
for (i in names(hmTemp$colColorPresets)){
if (i %in% hmTemp$colDataName){
for (j in names(hmTemp$colColorPresets[[i]])){
if(!is.null(input[[paste0('hmcol', i, j)]])){
hmTemp$colColorPresets[[i]][[j]] <- input[[paste0('hmcol', i, j)]]
}
}
}
}
})
observe({
for (i in names(hmTemp$rowColorPresets)){
if (i %in% hmTemp$rowDataName){
for (j in names(hmTemp$rowColorPresets[[i]])){
if(!is.null(input[[paste0('hmrow', i, j)]])){
hmTemp$rowColorPresets[[i]][[j]] <- input[[paste0('hmrow', i, j)]]
}
}
}
}
})
# Heatmap: Others ####
output$hmColSplitUI <- renderUI({
selectInput(
'hmColSplit',
"Split columns (cell) by (Leave this for not splitting)",
hmTemp$colDataName, multiple = TRUE, selected = hmTemp$colSplitBy
)
})
output$hmRowSplitUI <- renderUI({
selectInput(
'hmRowSplit',
"Split rows (feature) by (Leave this for not splitting)",
hmTemp$rowDataName, multiple = TRUE, selected = hmTemp$rowSplitBy
)
})
observeEvent(input$hmColSplit, {
hmTemp$colSplitBy <- input$hmColSplit
})
observeEvent(input$hmRowSplit, {
hmTemp$rowSplitBy <- input$hmRowSplit
})
output$hmTrimUI <- renderUI({
if(!is.null(vals$counts) && !is.null(input$hmAssay)){
# This might be slow when running with real data
mat <- as.matrix(assay(vals$counts, input$hmAssay))
if(isTRUE(input$hmScale)){
mat <- as.matrix(computeZScore(mat))
}
sliderInput("hmTrim", "Trim", min = floor(min(mat)),
max = ceiling(max(mat)), value = c(-2, 2), step = 0.5)
}
})
# Heatmap: Color Scheme ####
observe({
# Palette preset coding refers:
# https://stackoverflow.com/a/52552008/13676674
vals$hmCSURL <- session$registerDataObj(
name = 'uniquename1',
data = vals$hmCSPresets,
filter = function(data, req) {
query <- parseQueryString(req$QUERY_STRING)
palette <- query$palette
cols <- data[[palette]]
image <- tempfile()
tryCatch({
png(image, width = 75, height = 25, bg = 'transparent')
par(mar = c(0, 0, 0, 0))
barplot(rep(1, length(cols)), col = cols, axes = FALSE)
},finally = dev.off())
shiny:::httpResponse(
200, 'image/png',readBin(image, 'raw', file.info(image)[,'size'])
)
}
)
updateSelectizeInput(
session, 'hmCSPalette', server = TRUE,
choices = names(vals$hmCSPresets),
selected = "RWB",
options = list(
render = I(
sprintf(
"{
option: function(item, escape) {
return '<div><img width=\"75\" height=\"25\" ' +
'src=\"%s&palette=' + escape(item.value) + '\" />' +
escape(item.value) + '</div>';
}
}",
vals$hmCSURL
)
)
)
)
})
observeEvent(input$hmCSPalette, {
if(!input$hmCSPalette == ""){
lowColor <- vals$hmCSPresets[[input$hmCSPalette]][1]
colourpicker::updateColourInput(session, 'hmCSLow', value = lowColor)
}
if(!input$hmCSPalette == ""){
mediumColor <- vals$hmCSPresets[[input$hmCSPalette]][2]
colourpicker::updateColourInput(session, 'hmCSMedium', value = mediumColor)
}
if(!input$hmCSPalette == ""){
highColor <- vals$hmCSPresets[[input$hmCSPalette]][3]
colourpicker::updateColourInput(session, 'hmCSHigh', value = highColor)
}
})
# Heatmap: Final run ####
observeEvent(input$plotHeatmap, {
if (is.null(vals$counts)){
shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
} else {
# Move all plotting process into alert callback, thus auto-re-render can
# be avoided while tuning parameters.
shinyalert(
title = "Confirm",
text = "Large dataset might take time to rerun. Are you sure with the parameters?",
type = "warning",
showCancelButton = TRUE,
confirmButtonText = "Plot",
cancelButtonText = "Check Once More",
callbackR = function(x){
if(isTRUE(x)){
withBusyIndicatorServer("plotHeatmap", {
if(!is.null(hmTemp$colDataName)){
cellAnnColor <- list()
for(i in hmTemp$colDataName){
uniqs <- as.vector(unique(colData(hmTemp$sce)[[i]]))
uniqs[is.na(uniqs)] <- 'NA'
if (i %in% names(hmTemp$colColorPresets)) {
cellAnnColor[[i]] <- hmTemp$colColorPresets[[i]]
} else if (length(uniqs) <= 12) {
cellAnnColor[[i]] <- vector()
for(j in uniqs){
inputId <- paste0('hmcol', i, j)
cellAnnColor[[i]] <- c(cellAnnColor[[i]], input[[inputId]])
}
names(cellAnnColor[[i]]) <- uniqs
} else {
if(is.numeric(colData(hmTemp$sce)[[i]])){
if(input[[paste0('hmcol', i, 'type')]] == 'Continuous'){
cFun <- circlize::colorRamp2(
c(min(colData(hmTemp$sce)[[i]]),
max(colData(hmTemp$sce)[[i]])),
c(input[[paste0('hmcol', i, 'Low')]],
input[[paste0('hmcol', i, 'High')]])
)
cellAnnColor[[i]] <- cFun
} else {
c <- distinctColors(length(uniqs))
names(c) <- uniqs
cellAnnColor[[i]] <- c
}
}
}
}
} else {
cellAnnColor <- NULL
}
if(!is.null(hmTemp$rowDataName)){
geneAnnColor <- list()
for(i in hmTemp$rowDataName){
uniqs <- as.vector(unique(rowData(hmTemp$sce)[[i]]))
if (i %in% names(hmTemp$rowColorPresets)) {
geneAnnColor[[i]] <- hmTemp$rowColorPresets[[i]]
} else if(length(uniqs) <= 12){
geneAnnColor[[i]] <- vector()
for(j in uniqs){
inputId <- paste0('hmrow', i, j)
geneAnnColor[[i]] <- c(geneAnnColor[[i]], input[[inputId]])
}
names(geneAnnColor[[i]]) <- uniqs
} else {
if(is.numeric(rowData(hmTemp$sce)[[i]])){
if(input[[paste0('hmrow', i, 'type')]] == 'Continuous'){
cFun <- circlize::colorRamp2(
c(min(rowData(hmTemp$sce)[[i]]),
max(rowData(hmTemp$sce)[[i]])),
c(input[[paste0('hmrow', i, 'Low')]],
input[[paste0('hmrow', i, 'High')]])
)
geneAnnColor[[i]] <- cFun
} else {
c <- distinctColors(length(uniqs))
names(c) <- uniqs
geneAnnColor[[i]] <- c
}
}
}
}
} else {
geneAnnColor <- NULL
}
hmAddLabel <- list(cell = FALSE, gene = FALSE)
if(!is.null(input$hmAddLabel)){
if("1" %in% input$hmAddLabel){
if(input$hmAddCellLabel == "Default cell IDs"){
hmAddLabel$cell <- TRUE
} else {
hmAddLabel$cell <- input$hmAddCellLabel
}
}
if("2" %in% input$hmAddLabel){
if(input$hmAddGeneLabel == "Default feature IDs"){
hmAddLabel$gene <- TRUE
} else {
hmAddLabel$gene <- input$hmAddGeneLabel
}
}
}
hmShowDendro <- c(FALSE, FALSE)
hmShowDendro[as.numeric(input$hmShowDendro)] <- TRUE
#if(is.null(hmTemp$rowSplitBy)){
# hmRowSplit <- NULL
#} else {
# hmRowSplit <- hmTemp$rowSplitBy
#}
#if(is.null(hmTemp$colSplitBy)){
# hmColSplit <- NULL
#} else {
# hmColSplit <- hmTemp$colSplitBy
#}
cs <- circlize::colorRamp2(
c(input$hmTrim[1], mean(input$hmTrim), input$hmTrim[2]),
c(input$hmCSLow, input$hmCSMedium, input$hmCSHigh)
)
output$Heatmap <- renderPlot({
isolate({
plotSCEHeatmap(
inSCE = hmTemp$sce, useAssay = input$hmAssay, colorScheme = cs,
featureIndex = hmTemp$geneIndex, cellIndex = hmTemp$cellIndex,
rowDataName = hmTemp$rowDataName, colDataName = hmTemp$colDataName,
rowSplitBy = hmTemp$rowSplitBy, colSplitBy = hmTemp$colSplitBy,
rowLabel = hmAddLabel$gene, colLabel = hmAddLabel$cell,
rowDend = hmShowDendro[2], colDend = hmShowDendro[1],
scale = input$hmScale, trim = input$hmTrim,
width = unit(20, 'cm'), height = unit(20, 'cm'),
featureAnnotationColor = geneAnnColor,
cellAnnotationColor = cellAnnColor
)
})
}, height = 800)
})
}
}
)
}
})
# Bubbleplot: Final run ####
observeEvent(input$bpAssay, {
req(vals$counts)
updateSelectInput(session, "bpAssay",
label = "Select Assay")
})
output$bpClusterUI <- renderUI({
req(vals$counts)
selectInput(
'bpCluster',
"",
colnames(colData(vals$counts)), multiple = FALSE, width = '550px')
})
observe({
req(vals$counts)
if (!is.null(metadata(vals$counts)$featureDisplay) && metadata(vals$counts)$featureDisplay %in% names(rowData(vals$counts))) {
featureDisplayValue <- metadata(vals$counts)$featureDisplay
updateSelectizeInput(session, "bpFeatures", choices = rowData(vals$counts)[[featureDisplayValue]], server = TRUE)
}
})
observeEvent(input$plotBubbleplot, {
req(vals$counts)
output$Bubbleplot <- renderPlot({
isolate({
plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures,
displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle,
xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
})
})
})
observeEvent(input$updateBubbleplot, {
req(vals$counts)
output$Bubbleplot <- renderPlot({
isolate({
plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures,
displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle,
xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
})
})
})
# #COG For BubblePlot
# observeEvent(input$closeDropDownBubble, {
# session$sendCustomMessage("close_dropDownBubble", "")
# })
#
# observeEvent(input$bubblePlot, {
# req(vals$counts)
# choice <- NULL
# if (input$bubbleVisChoicesType == 1) {
# # Use result
# if (is.null(input$bubbleVisRes) ||
# input$bubbleVisRes == "") {
# shinyalert::shinyalert("Error!", "Select the clusters to plot",
# type = "error")
# }
# choice <- input$bubbleVisRes
# } else if (input$bubbleVisChoicesType == 2) {
# # Use colData
# if (is.null(input$bubbleVisCol) ||
# input$bubbleVisCol == "") {
# shinyalert::shinyalert("Error!", "Select the clusters to plot",
# type = "error")
# }
# choice <- input$bubbleVisCol
# }
# if (is.null(input$bubbleVisReddim) || input$bubbleVisReddim == "") {
# shinyalert::shinyalert("Error!",
# "No reduction selected. Select one or run dimension reduction first",
# type = "error")
# }
# if (!is.null(choice) && choice != "" &&
# !is.null(input$bubbleVisReddim) && input$bubbleVisReddim != "") {
# output$bubbleVisPlot <- renderPlotly({
# isolate({
# plotSCEDimReduceColData(inSCE = vals$counts,
# colorBy = choice,
# conditionClass = "factor",
# reducedDimName = input$bubbleVisReddim,
# labelClusters = TRUE,
# dim1 = 1, dim2 = 2,
# legendTitle = choice)
# })
# })
# }
# session$sendCustomMessage("close_dropDownBubble", "")
#-----------------------------------------------------------------------------
# Page 4: Batch Correction ####
#-----------------------------------------------------------------------------
observeEvent(input$closeDropDownBC, {
session$sendCustomMessage("close_dropDownBC", "")
})
observeEvent(input$batchCorrMethods, {
if (!is.null(vals$counts) &&
!is.null(input$batchCorrMethods)) {
# What type of assays are required, according their docs
# ComBatSeq - counts
# BBKNN - filtered, normalized, and scaled
# fastMNN - log-expression
# Limma - log-expression
# MNN - log-expression
# scanorama - normalized, log1p
# scMerge - logcounts
# zinbwave - counts
bc.recommended <- NULL
method.log <- c("FastMNN", "Limma", "MNN")
method.scale <- c("BBKNN")
method.raw <- c("ZINBWaVE", "ComBatSeq")
if (is.null(input$batchCorrMethods)) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.log) {
bc.recommended <- c("transformed", "normalized")
} else if (input$batchCorrMethods %in% method.raw) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.scale) {
bc.recommended <- "scaled"
}
updateSelectInputTag(session, "batchCorrAssay",
label = "Select Assay to Correct:",
choices = assayNames(vals$counts),
recommended = bc.recommended)
}
})
output$batchCheckResUI <- renderUI({
selectInput("batchCheckCorrName", "Corrected Matrix",
c(names(vals$batchRes)))
})
observeEvent(input$plotBatchCheck, {
if(!is.null(vals$counts) &&
!is.null(input$batchCheckCorrName) &&
input$batchCheckVar != input$batchCheckCond){
withBusyIndicatorServer("plotBatchCheck", {
## Generals
if(input$batchCheckCond == "None"){
shapeBy <- NULL
} else {
shapeBy <- input$batchCheckCond
}
## Original assay PCA
oriAssayPCAName <- paste0(input$batchCheckOrigAssay, "_PCA")
if(!oriAssayPCAName %in% names(reducedDims(vals$counts))){
vals$counts <- scaterPCA(vals$counts,
useAssay = input$batchCheckOrigAssay,
reducedDimName = oriAssayPCAName)
updateReddimInputs()
}
resName <- input$batchCheckCorrName
## Corrected assay/altExp PCA
if (vals$batchRes[[resName]] == 'assay'){
corrAssayPCAName = paste0(resName, "_PCA")
vals$counts <- scaterPCA(vals$counts, useAssay = resName,
reducedDimName = corrAssayPCAName)
updateReddimInputs()
} else if (vals$batchRes[[resName]] == 'altExp'){
ae <- altExp(vals$counts, resName)
corrAltExpPCAName <- paste0(resName, "_PCA")
ae <- scaterPCA(ae, useAssay = resName,
reducedDimName = corrAltExpPCAName)
reducedDim(vals$counts, corrAltExpPCAName) <-
reducedDim(ae, corrAltExpPCAName)
updateReddimInputs()
}
## Update plots
output$batchOriVars <- renderPlot({
isolate({
plotBatchVariance(inSCE = vals$counts,
useAssay = input$batchCheckOrigAssay,
batch = input$batchCheckVar,
condition = shapeBy)
})
})
output$batchOriPCA <- renderPlot({
isolate({
plotSCEDimReduceColData(vals$counts, colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = oriAssayPCAName,
dim1 = 1, dim2 = 2,
title = paste0("Original ",
input$batchCheckOrigAssay,
" PCA"))
})
})
output$batchCorrVars <- renderPlot({
isolate({
if (vals$batchRes[[resName]] == 'reddim'){
plotBatchVariance(inSCE = vals$counts, useReddim = resName,
batch = input$batchCheckVar,
condition = shapeBy)
} else if (vals$batchRes[[resName]] == 'assay'){
plotBatchVariance(inSCE = vals$counts, useAssay = resName,
batch = input$batchCheckVar,
condition = shapeBy)
} else if (vals$batchRes[[resName]] == 'altExp'){
plotBatchVariance(inSCE = vals$counts, useAltExp = resName,
batch = input$batchCheckVar,
condition = shapeBy)
}
})
})
output$batchCorrReddim <- renderPlot({
isolate({
if (vals$batchRes[[resName]] == 'reddim'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = resName,
conditionClass = "character",
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
} else if (vals$batchRes[[resName]] == 'assay'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = corrAssayPCAName,
conditionClass = "character",
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
} else if (vals$batchRes[[resName]] == 'altExp'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = corrAltExpPCAName,
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
}
})
})
})
}
session$sendCustomMessage("close_dropDownBC", "")
})
#observeEvent(input$BBKNNRun, withConsoleMsgRedirect(
# msg = "Please wait while BBKNN method for batch correction is being executed. See console log for progress.",
# {
# req(vals$counts)
# saveassayname <- gsub(" ", "_", input$BBKNNSaveReddim)
# message(date(), " ... Running BBKNN batch correction method")
# vals$counts <- runBBKNN(vals$counts,
# useAssay = input$batchCorrAssay,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$BBKNNNComp)
# message(date(), " ... BBKNN finished")
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# }
#))
observeEvent(input$combatRun, withConsoleMsgRedirect(
msg = "Please wait while CombatSeq method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
#check for zeros
if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
stop("Rows with a sum of zero found. Filter data to continue.")
}
saveassayname <- gsub(" ", "_", input$combatSaveAssay)
if (input$combatKnownCT == "Yes") {
cov <- input$combatCond
} else {
cov <- NULL
}
if (input$combatCTBalance == "Yes") {
useSVA <- FALSE
} else {
useSVA <- TRUE
}
if (input$combatBioCond == "None") {
combatBioCond <- NULL
} else {
combatBioCond <- input$combatBioCond
}
message(date(), " ... Running ComBatSeq batch correction method")
vals$counts <- runComBatSeq(inSCE = vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
covariates = cov,
bioCond = combatBioCond,
useSVA = useSVA,
assayName = saveassayname,
shrink = input$combatShrink,
shrinkDisp = input$combatShrinkDisp,
nGene = input$combatNGene )
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
message(date(), " ... CamBatSeq finished")
}
))
observeEvent(input$FastMNNRun, withConsoleMsgRedirect(
msg = "Please wait while FASTMNN method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$FastMNNSaveReddim)
if(isTRUE(input$FastMNNPcInput)){
fmnnAssay <- input$FastMNNReddim
} else {
fmnnAssay <- input$batchCorrAssay
}
message(date(), " ... Running FastMNN batch correction method")
vals$counts <- runFastMNN(vals$counts,
useAssay = fmnnAssay,
batch = input$batchCorrVar,
reducedDimName = saveassayname,
pcInput = input$FastMNNPcInput
)
message(date(), " ... FastMNN finished")
vals$batchRes[[saveassayname]] <- 'reddim'
updateReddimInputs()
}
))
# TODO: Remember to follow the logging format as other batch correction chunks
# When putting Harmony back
# observeEvent(input$HarmonyRun, {
# if (is.null(vals$counts)){
# shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
# } else {
# withBusyIndicatorServer("HarmonyRun", {
# saveassayname <- gsub(" ", "_", input$HarmonySaveReddim)
# if(isTRUE(input$HarmonyPcInput)){
# useAssay <- input$HarmonyReddim
# } else {
# useAssay <- input$batchCorrAssay
# }
# if(is.na(as.numeric(input$HarmonyTheta))){
# stop("Theta value must be numeric.")
# } else {
# theta <- as.numeric(input$HarmonyTheta)
# }
# vals$counts <- runHarmony(vals$counts, useAssay = useAssay,
# pcInput = input$HarmonyPcInput,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$HarmonyNComp,
# theta = theta, nIter = input$HarmonyNIter)
# shinyalert::shinyalert('Success!', 'Harmony completed.',
# type = 'success')
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# })
# }
# })
observeEvent(input$limmaRun, withConsoleMsgRedirect(
msg = "Please wait while Limma method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
message(date(), " ... Running Limma batch correction method")
vals$counts <- runLimmaBC(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
assayName = saveassayname)
message(date(), " ... Limma batch correction finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
# TODO: Remember to follow the logging format as other batch correction chunks
# When putting LIGER back
#observeEvent(input$ligerRun, {
# if (is.null(vals$counts)){
# shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
# }
# else{
# withBusyIndicatorServer("ligerRun", {
# #check for zeros
# if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
# shinyalert::shinyalert("Error!", "Rows with a sum of zero found. Filter data to continue.", type = "error")
# } else {
# saveassayname <- gsub(" ", "_", input$ligerSaveReddim)
# vals$counts <-
# runLIGER(inSCE = vals$counts,
# useAssay = input$batchCorrAssay,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$ligerNComp,
# lambda = input$ligerLambda,
# resolution = input$ligerResolution)
# shinyalert::shinyalert('Success!', 'LIGER completed.',
# type = 'success')
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# }
# })
# }
#})
observeEvent(input$MNNRun, withConsoleMsgRedirect(
msg = "Please wait while MNN method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$MNNSaveAssay)
message(date(), " ... Running MNN batch correction method")
vals$counts <- runMNNCorrect(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
k = input$MNNK, sigma = input$MNNSigma,
assayName = saveassayname)
message(date(), " ... MNN finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
observeEvent(input$scnrmRun, withConsoleMsgRedirect(
msg = "Please wait while Scanorama method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$scnrmSaveAssay)
message(date(), " ... Running Scanorama batch correction method")
vals$counts <- runSCANORAMA(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
SIGMA = input$scnrmSIGMA,
ALPHA = input$scnrmALPHA,
KNN = input$scnrmKNN,
assayName = saveassayname)
message(date(), " ... Scanorama finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
observeEvent(input$batchCorrVar, {
req(vals$counts)
req(input$batchCorrVar)
nBatch <- length(unique(colData(vals$counts)[[input$batchCorrVar]]))
output$scMergeNBatch <- renderUI({
span(paste0("Please input ", nBatch, " integer(s), separated by ','."))
})
})
observeEvent(input$scMergeRun, withConsoleMsgRedirect(
msg = "Please wait while scMerge method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$scMergeSaveAssay)
if(input$scMergeSEGOpt == 1){
seg <- NULL
} else if(input$scMergeSEGOpt == 2){
data("SEG")
seg <- SEG[[input$scMergeSEGSpecies]]
} else {
seg <- str_trim(scan(text = input$scMergeSEGCustom,
sep='\n', what = 'character'))
}
if(isTRUE(input$scMergeAutoKmk)){
kmk <- NULL
} else {
kmk <- scan(text = input$scMergeUserKmk, sep=',')
}
message(date(), " ... Running scMerge batch correction method")
vals$counts <- runSCMerge(inSCE = vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
cellType = input$scMergeCT,
seg = seg, kmeansK = kmk,
assayName = saveassayname
)
message(date(), " ... scMerge finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
output$Srt3IntNAnchUI <- renderUI({
if(!is.null(vals$counts)){
ngene <- nrow(vals$counts)
tagList(
numericInput('Srt3IntNAnch', "Number of anchors:",
value = ngene, min = 30, max = ngene, step = 1),
numericInput('Srt3IntKWeight', "kWeight:",
value = 0, min = 0, step = 1),
numericInput('Srt3IntKFilter', "kFilter:",
value = 0, min = 0, step = 1),
numericInput('Srt3IntNDims', "Number of Dimensions:",
value = 2, min = 2, step = 1)
)
}
})
observeEvent(input$Srt3IntRun, withConsoleMsgRedirect(
msg = "Please wait while Seurat3 integration for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$Srt3IntSaveAssay)
message(date(), " ... Running Seurat3 integration method")
vals$counts <- runSeuratIntegration(
inSCE = vals$counts,
batch = input$batchCorrVar,
newAssayName = saveassayname,
kAnchor = input$Srt3IntNAnch,
kWeight = input$Srt3IntKWeight,
kFilter = input$Srt3IntKFilter,
ndims = input$Srt3IntNDims)
message(date(), " ... Seurat3 integration finished")
vals$batchRes[[saveassayname]] <- 'altExp'
}
))
output$zinbwaveNHvgUI <- renderUI({
if(!is.null(vals$counts)){
ngenes <- nrow(vals$counts)
zwdefault <- min(ngenes, 1000)
numericInput('zinbwaveNHVG', 'Number of highly variable genes to use:',
value = zwdefault, max = ngenes)
}
})
output$zinbwaveEpsUI <- renderUI({
if(!is.null(vals$counts)){
ngenes <- nrow(vals$counts)
zwdefault <- min(ngenes, 1000)
numericInput('zinbwaveEps', 'Epsilon value:',
value = zwdefault, max = ngenes)
}
})
observeEvent(input$zinbwaveRun, withConsoleMsgRedirect(
msg = "Please wait while ZINBWaVE method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
message(date(), " ... Running ZINBWaVE batch correction method")
vals$counts <- runZINBWaVE(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
reducedDimName = saveassayname,
epsilon = input$zinbwaveEps,
nHVG = input$zinbwaveNHVG,
nIter = input$zinbwaveNIter,
nComponents = input$zinbwaveNComp
)
message(date(), " ... ZINBWaVE finished")
vals$batchRes[[saveassayname]] <- 'reddim'
updateReddimInputs()
}
))
#-----------------------------------------------------------------------------
# Page 4.1: Feature Selection ####
#-----------------------------------------------------------------------------
observeEvent(input$hvgMethodFS,{
req(vals$counts)
updateAssayInputs()
})
updateHVGMetricSelection <- function(selected = NULL){
metricList <- lapply(metadata(vals$counts)$sctk$runFeatureSelection,
function(x){x$useAssay})
metricOptions <- names(metricList)
toggleState("hvgMetricSelect", !is.null(metricOptions))
toggleState("hvgNumberSelect", !is.null(metricOptions))
toggleState("hvgSubsetName", !is.null(metricOptions))
toggleState("hvgSubsetRun", !is.null(metricOptions))
toggleState("hvgPlotMethod", !is.null(metricOptions))
toggleState("updatePlotFS", !is.null(metricOptions))
if (!is.null(metricOptions)) {
names(metricOptions) <- paste0(names(metricList), " - ", metricList)
updateSelectInput(session, "hvgPlotMethod", choices = metricOptions,
selected = selected)
updateSelectInput(session, "hvgMetricSelect", choices = metricOptions,
selected = selected)
}
}
updateHVGListSelection <- function(selected = NULL) {
featureSubsets <- names(metadata(vals$counts)$sctk$featureSubsets)
toggleState("hvgPlotSubsetSelect", !is.null(featureSubsets))
toggleState("hvgPlotNLabel", !is.null(featureSubsets))
if (!is.null(featureSubsets)) {
if (is.null(selected)) {
if (!is.null(featureSubsets)) {
selected <- featureSubsets[1]
} else {
selected <- "None"
}
}
updateSelectInput(session, "hvgPlotSubsetSelect",
choices = c("None", featureSubsets),
selected = selected)
updateSelectInput(session, "dimRedHVGSelect",
choices = c("None", featureSubsets),
selected = selected)
updateSelectInput(session, "hvg_tsneUmap",
choices = c("None", featureSubsets),
selected = selected)
}
}
observeEvent(input$findHvgButtonFS, withConsoleMsgRedirect(
msg = "Please wait while variability is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- runFeatureSelection(inSCE = vals$counts,
useAssay = input$assaySelectFS_Norm,
method = input$hvgMethodFS)
updateHVGMetricSelection(selected = input$hvgMethodFS)
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgMethodFS,
hvgNumber = 0,
labelsCount = 0)
})
})
}
))
observeEvent(c(input$hvgMetricSelect, input$hvgNumberSelect), {
# Auto made default hvgListName
subsetName <- paste0("HVG_", input$hvgMetricSelect, input$hvgNumberSelect)
updateTextInput(session, "hvgSubsetName", value = subsetName)
})
observeEvent(input$hvgSubsetRun, withConsoleMsgRedirect(
msg = "Please wait feature subset is being created. See console log for progress.",
{
req(vals$counts)
if (input$hvgMetricSelect == "") {
stop("Must calculate variability before selection.")
} else if (input$hvgSubsetName == "") {
stop("Must specify a name for the HVG list.")
} else if (is.na(input$hvgNumberSelect)) {
stop("Must set the number of HVGs for the selection.")
} else {
vals$counts <- setTopHVG(vals$counts,
method = input$hvgMetricSelect,
hvgNumber = input$hvgNumberSelect,
featureSubsetName = input$hvgSubsetName)
updateHVGListSelection(selected = input$hvgSubsetName)
updateHVGMetricSelection(selected = input$hvgMetricSelect)
updateNumericInput(session, "hvgPlotNSelect", value = input$hvgNumberSelect)
featureDisplay <- NULL
if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
featureDisplay <- input$hvgPlotFeatureDisplay
}
# After selection, update visualization part
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgMetricSelect,
useFeatureSubset = input$hvgSubsetName,
labelsCount = 20,
featureDisplay = featureDisplay)
})
})
output$hvgOutputFS <- renderText({
isolate({
getTopHVG(inSCE = vals$counts,
useFeatureSubset = input$hvgSubsetName,
featureDisplay = featureDisplay)
})
})
}
}))
observeEvent(input$updatePlotFS, {
req(vals$counts)
req(metadata(vals$counts)$sctk$runFeatureSelection)
featureDisplay <- NULL
if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
featureDisplay <- input$hvgPlotFeatureDisplay
}
usefeatureSubset <- input$hvgPlotSubsetSelect
if (input$hvgPlotSubsetSelect == "None") {
usefeatureSubset <- NULL
}
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgPlotMethod,
useFeatureSubset = usefeatureSubset,
labelsCount = input$hvgPlotNLabel,
featureDisplay = featureDisplay)
})
})
output$hvgOutputFS <- renderText({
isolate({
if (!is.null(usefeatureSubset)) {
getTopHVG(inSCE = vals$counts,
useFeatureSubset = usefeatureSubset,
featureDisplay = featureDisplay)
} else {
""
}
})
})
session$sendCustomMessage("close_dropDownFS", "")
})
observeEvent(input$closeDropDownFS, {
session$sendCustomMessage("close_dropDownFS", "")
})
#-----------------------------------------------------------------------------
# Page 5.1: Differential Expression ####
#-----------------------------------------------------------------------------
observeEvent(input$deMethod, {
if (!is.null(vals$counts)) {
if (is.null(input$deMethod)) {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed", "normalized"),
redDims = TRUE)
} else if (input$deMethod == "DESeq2") {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled"),
recommended = c("raw"))
} else {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed", "normalized"),
redDims = TRUE)
}
}
})
## DE - Thresholding Vis ####
observeEvent(input$deViewThresh, withConsoleMsgRedirect(
msg = "Please wait while threshold is being plotted. See console log for progress.",
{
req(vals$counts)
req(input$deAssay)
shinyjs::showElement(id= "deThreshpanel")
message(paste0(date(), " ... Plotting thresholding"))
# MAST style sanity check for whether logged or not
x <- expData(vals$counts, input$deAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnThresh <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnThresh <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
suppressMessages({
thres.grob <- plotMASTThresholdGenes(inSCE = vals$counts,
useAssay = input$deAssay,
check_sanity = FALSE,
isLogged = isLogged,
doPlot = FALSE)
})
nSub <- tail(strsplit(thres.grob$childrenOrder, split = '-'),
n = 1)[[1]][3]
plotHeight <- ceiling(as.numeric(nSub) / 4) * 240
output$deThreshPlotDiv <- renderUI({
div(
style = paste0("height: ", plotHeight, "px;"),
plotOutput("deThreshplot"))
})
output$deThreshplot <- renderPlot({
grid.draw(thres.grob)
}, height = plotHeight)
updateActionButton(session, "deViewThresh", "Refresh")
}
))
observeEvent(input$deHideThresh, {
shinyjs::hideElement(id= "deThreshpanel")
updateActionButton(session, "deViewThresh", "View Thresholding")
})
## DE - condition determination method1 ####
output$deC1G1UI <- renderUI({
if(!is.null(vals$counts) &
!input$deC1Class == "None"){
classCol <- colData(vals$counts)[[input$deC1Class]]
classChoices <- sort(as.vector(unique(classCol)))
selectInput(inputId = "deC1G1", label = "Select Condition(s)",
choices = classChoices, multiple = TRUE)
} else {
selectInput(inputId = "deC1G1", label = "Select Condition(s)",
choices = NULL, multiple = TRUE)
}
})
output$deC1G2UI <- renderUI({
if(!is.null(vals$counts) &
!input$deC1Class == "None"){
classCol <- colData(vals$counts)[[input$deC1Class]]
classChoices <- sort(as.vector(unique(classCol)))
selectInput(inputId = "deC1G2", label = "Select Condition(s)",
choices = classChoices, multiple = TRUE)
} else {
selectInput(inputId = "deC1G2", label = "Select Condition(s)",
choices = NULL, multiple = TRUE)
}
})
output$deC1G1CellCheckUI <- renderUI({
if(!is.null(input$deC1G1) &
length(input$deC1G1) > 0){
g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
g1Cells <- colnames(vals$counts)[g1Idx]
g1CellsText <- paste(g1Cells, collapse = "\n")
textAreaInput("deC1G1CellCheck", "Cells selected:", g1CellsText,
height = '100px', placeholder = "Nothing selected")
} else {
textAreaInput("deC1G1CellCheck", "Cells selected:", NULL,
height = '100px', placeholder = "Nothing selected")
}
})
output$deC1G2CellCheckUI <- renderUI({
if(!is.null(input$deC1G2) &
length(input$deC1G2) > 0){
g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
g2Cells <- colnames(vals$counts)[g2Idx]
g2CellsText <- paste(g2Cells, collapse = "\n")
textAreaInput("deC1G2CellCheck", "Cells selected:", g2CellsText,
height = '100px',
placeholder = "Leave unselected for all the others.")
} else {
textAreaInput("deC1G2CellCheck", "Cells selected:", NULL,
height = '100px',
placeholder = "Leave unselected for all the others.")
}
})
output$deC1G1NCell <- renderUI({
if(!is.null(input$deC1G1CellCheck)){
if(!input$deC1G1CellCheck == ""){
cellList <- str_trim(scan(text = input$deC1G1CellCheck,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " cell(s) selected.")
span(msg, style = 'margin-left:10px')
})
output$deC1G2NCell <- renderUI({
if(!is.null(input$deC1G2CellCheck)){
if(!input$deC1G2CellCheck == ""){
cellList <- str_trim(scan(text = input$deC1G2CellCheck,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " cell(s) selected.")
span(msg, style = 'margin-left:10px')
})
## DE - condition determination method2 ####
## condition 1 table operation vvvv
output$deC2G1Table <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- lapply(colData(vals$counts),
function(i){
if(is.character(i) && !length(unique(i)) == length(i)){
return(as.factor(i))
} else if(is.integer(i) &&
!length(unique(i)) == length(i)){
return(as.factor(i))
} else {
return(i)
}
})
df <- data.frame(df, row.names = colnames(vals$counts))
DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
}
}, server = TRUE)
deC2G1Table_proxy <- DT::dataTableProxy("deC2G1Table")
observeEvent(input$deC2G1Col, {
colNames <- names(colData(vals$counts))
showIdx <- which(colNames %in% input$deC2G1Col)
DT::showCols(deC2G1Table_proxy, showIdx, reset = TRUE)
})
observeEvent(input$deC2G1Table_addAll, {
DT::selectRows(deC2G1Table_proxy,
sort(unique(c(input$deC2G1Table_rows_selected,
input$deC2G1Table_rows_all))))
})
observeEvent(input$deC2G1Table_clear, {
DT::selectRows(deC2G1Table_proxy, NULL)
})
output$deC2G1info <- renderUI({
nCell <- length(input$deC2G1Table_rows_selected)
p(paste0("Totally ", nCell, " cells selected for ", input$deG1Name))
})
## condition 1 table operation ^^^^
## condition 2 table operation vvvv
output$deC2G2Table <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- lapply(colData(vals$counts),
function(i){
if(is.character(i) && !length(unique(i)) == length(i)){
return(as.factor(i))
} else if(is.integer(i) &&
!length(unique(i)) == length(i)){
return(as.factor(i))
} else {
return(i)
}
})
df <- data.frame(df, row.names = colnames(vals$counts))
DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
}
}, server = TRUE)
deC2G2Table_proxy <- DT::dataTableProxy("deC2G2Table")
observeEvent(input$deC2G2Col, {
colNames <- names(colData(vals$counts))
showIdx <- which(colNames %in% input$deC2G2Col)
DT::showCols(deC2G2Table_proxy, showIdx, reset = TRUE)
})
observeEvent(input$deC2G2Table_addAll, {
DT::selectRows(deC2G2Table_proxy,
sort(unique(c(input$deC2G2Table_rows_selected,
input$deC2G2Table_rows_all))))
})
observeEvent(input$deC2G2Table_clear, {
DT::selectRows(deC2G2Table_proxy, NULL)
})
output$deC2G2info <- renderUI({
nCell <- length(input$deC2G2Table_rows_selected)
p(paste0("Totally ", nCell, " cells selected for ", input$deG2Name))
})
## condition 2 table operation ^^^^
## DE - condition determination method3 ####
output$deC3G1NCell <- renderUI({
if(!is.null(input$deC3G1Cell)){
if(!input$deC3G1Cell == ""){
cellList <- str_trim(scan(text = input$deC3G1Cell,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
span(msg, style = 'margin-left:10px')
})
output$deC3G2NCell <- renderUI({
if(!is.null(input$deC3G2Cell)){
if(!input$deC3G2Cell == ""){
cellList <- str_trim(scan(text = input$deC3G2Cell,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
span(msg, style = 'margin-left:10px')
})
# DE run analysis ####
runDEfromShiny <- function(overwrite){
if (input$deAssay %in% assayNames(vals$counts) &
!input$deAssay %in% reducedDimNames(vals$counts)) {
deUseAssay <- input$deAssay
deUseReducedDim <- NULL
} else if (!input$deAssay %in% assayNames(vals$counts) &
input$deAssay %in% reducedDimNames(vals$counts)) {
deUseAssay <- NULL
deUseReducedDim <- input$deAssay
} else {
stop("Error in identifying input matrix")
}
if(input$deCondMethod == 1){
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
class = input$deC1Class,
classGroup1 = input$deC1G1,
classGroup2 = input$deC1G2,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$mastPosOnly,
overwrite = overwrite)
} else if(input$deCondMethod == 2){
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
index1 = input$deC2G1Table_rows_selected,
index2 = input$deC2G2Table_rows_selected,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$dePosOnly,
overwrite = overwrite)
} else {
g1CellList <- str_trim(scan(text = input$deC3G1Cell,
sep='\n', what = 'character', quiet = TRUE))
g1CellList <- sort(unique(g1CellList))
g2CellList <- str_trim(scan(text = input$deC3G2Cell,
sep='\n', what = 'character', quiet = TRUE))
g2CellList <- sort(unique(g2CellList))
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
index1 = g1CellList,
index2 = g2CellList,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$dePosOnly,
overwrite = overwrite)
}
updateDEAnalysisNames(selected = input$deAnalysisName)
colSplitBy <- "condition"
rowSplitBy <- "regulation"
x <- expData(vals$counts, input$deAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
isLogged <- TRUE
} else {
isLogged <- FALSE
updateCheckboxGroupInput(session, "deHMDoLog", selected = TRUE)
}
if (isTRUE(input$deHMShowRowLabel)) {
rowLabel <- TRUE
if (input$deHMrowLabel != "Rownames (Default)" &
is.null(deUseReducedDim)) {
rowLabel <- input$deHMrowLabel
}
} else {
rowLabel <- FALSE
}
message(date(), " ... Updating DE heatmap for analysis: ",
input$deAnalysisName)
output$deHeatmap <- renderPlot({
isolate({
plotDEGHeatmap(inSCE = vals$counts,
useResult = input$deAnalysisName,
onlyPos = input$dePosOnly,
fdrThreshold = input$deFDRThresh,
colSplitBy = colSplitBy,
rowSplitBy = rowSplitBy,
doLog = !isLogged,
rowLabel = rowLabel)
})
})
if (isFALSE(input$deVolcShowLabel)) {
labelTopN <- FALSE
} else if (isTRUE(input$deVolcShowLabel)) {
labelTopN <- input$deVolcTopN
}
message(date(), " ... Updating DE volcano plot for analysis: ",
input$deAnalysisName)
output$deVolcanoPlot <- renderPlot({
isolate({
plotDEGVolcano(inSCE = vals$counts,
useResult = input$deAnalysisName,
log2fcThreshold = input$deVolcLog2FC,
labelTopN = labelTopN,
fdrThreshold = input$deFDRThresh)
})
})
message(date(), " ... Updating DE violin plot for analysis: ",
input$deAnalysisName)
output$deViolinPlot <- renderPlot({
isolate({
plotDEGViolin(inSCE = vals$counts, useResult = input$deAnalysisName,
nrow = input$deVioNRow, ncol = input$deVioNCol,
labelBy = NULL,
check_sanity = FALSE, isLogged = isLogged)
})
})
message(date(), " ... Updating DE regression plot for analysis: ",
input$deAnalysisName)
output$deRegPlot <- renderPlot({
isolate({
plotDEGRegression(inSCE = vals$counts,
useResult = input$deAnalysisName,
nrow = input$deRegNRow,
ncol = input$deRegNCol,
labelBy = NULL,
check_sanity = FALSE,
isLogged = isLogged)
})
})
}
observeEvent(input$runDE, withConsoleMsgRedirect(
msg = "Please wait while DE analysis are being performed. See console log for progress.",
{
req(vals$counts)
if (input$deAnalysisName == "" ||
input$deG1Name == "" ||
input$deG2Name == "") {
stop("The name of the two conditions and the whole analysis have to be specified!")
}
allRes <- names(metadata(vals$counts)$diffExp)
if(input$deAnalysisName %in% allRes){
shinyalert(
"Warning",
"Entered differential experiment analysis name is already there.",
"warning", showCancelButton = TRUE,
confirmButtonText = "Overwrite",
callbackR = function(x){if(isTRUE(x)){runDEfromShiny(x)}})
} else {
runDEfromShiny(FALSE)
}
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-de", parent = session,
pa = TRUE, cv = TRUE)
}
))
updateDEAnalysisNames <- function(selected = NULL) {
deRes <- rev(names(metadata(vals$counts)$diffExp))
if (!is.null(deRes)) {
updateSelectInput(session, "deResSel", choices = deRes,
selected = selected)
updateSelectInput(session, "enrDEGSelect", choices = deRes,
selected = selected)
output$hmImpDEGUI <- renderUI({
selectInput('hmImpDEG', "Import results from analysis:", deRes)
})
} else {
output$hmImpDEGUI <- renderUI({
p("Differential expression analysis not performed yet.")
})
}
}
# DE: Result visualize ####
# Data table
# output$deResult <- DT::renderDataTable({
# if(!is.null(input$deResSel) &&
# !is.null(vals$counts)){
# metadata(vals$counts)$diffExp[[input$deResSel]]$result
# }
# }, filter = 'top')
observeEvent(input$deResSelUpdate, {
if (is.null(input$deResSel) ||
input$deResSel == "") {
shinyjs::disable("deDownload")
} else {
shinyjs::enable("deDownload")
callModule(
module = filterTableServer,
id = "deResult",
dataframe = getDEGTopTable(vals$counts, input$deResSel,
log2fcThreshold = NULL),
defaultFilterColumns = c('Log2_FC', 'FDR'),
defaultFilterOperators = c('>', '<'),
defaultFilterValues = c("1", "0.05"),
initialTopN = 100,
topText = "You can view the differentially epxressed features between the selected groups in the table below. And you can apply customized filters to filter the table accordingly."
)
}
})
output$deDownload <- downloadHandler(
filename = function() {
paste0("deResult_", input$deResSel, ".csv")
},
content = function(file) {
fullTable <- metadata(vals$counts)$diffExp[[input$deResSel]]$result
filteredTable <- fullTable[input$deResult_rows_all,]
filteredTable <- filteredTable[rowSums(is.na(filteredTable)) != ncol(filteredTable), ]
utils::write.csv(filteredTable, file, row.names = FALSE, )
}
)
# Volcano plot
observeEvent(input$closeDropDownDeVolcano, {
session$sendCustomMessage("close_dropDownDeVolcano", "")
})
observeEvent(input$deVolcShowLabel, {
if (isTRUE(input$deVolcShowLabel)) {
enable("deVolcTopN")
enable("deVolcFeatureDisplay")
} else if (isFALSE(input$deVolcShowLabel)) {
disable("deVolcTopN")
disable("deVolcFeatureDisplay")
}
})
observeEvent(list(input$dePlotVolcano, input$deResSelUpdate), {
req(vals$counts)
req(input$deResSel)
if (isFALSE(input$deVolcShowLabel)) {
labelTopN <- FALSE
} else if (isTRUE(input$deVolcShowLabel)) {
labelTopN <- input$deVolcTopN
}
featureDisplay <- NULL
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
if (input$deVolcFeatureDisplay != "Rownames (Default)" &
!is.null(useAssay)) {
featureDisplay <- input$deVolcFeatureDisplay
}
message(date(), " ... Updating DE volcano plot for analysis: ",
input$deResSel)
output$deVolcanoPlot <- renderPlot({
isolate({
plotDEGVolcano(inSCE = vals$counts,
useResult = input$deResSel,
labelTopN = labelTopN,
log2fcThreshold = input$deVolcLog2FC,
fdrThreshold = input$deVolcFDR,
featureDisplay = featureDisplay
)
})
})
session$sendCustomMessage("close_dropDownDeVolcano", "")
})
# Violin plot
output$deVioTotalUI <- renderUI({
topN <- input$deVioNRow * input$deVioNCol
p(as.character(topN))
})
observeEvent(input$closeDropDownDeViolin, {
session$sendCustomMessage("close_dropDownDeViolin", "")
})
observeEvent(list(input$dePlotVio, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == "" &&
!is.null(vals$counts)){
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
labelBy <- NULL
if (input$deVioLabel != "Rownames (Default)" &
!is.null(useAssay)) {
labelBy <- input$deVioLabel
}
# MAST style sanity check for whether logged or not
if (!is.null(useAssay)) {
x <- expData(vals$counts, useAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnViolin <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnViolin <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
} else {
isLogged <- TRUE
}
message(date(), " ... Updating DE violin plot for analysis: ",
input$deResSel)
output$deViolinPlot <- renderPlot({
isolate({
plotDEGViolin(inSCE = vals$counts, useResult = input$deResSel,
#threshP = input$deVioUseThresh,
nrow = input$deVioNRow, ncol = input$deVioNCol,
labelBy = labelBy, check_sanity = FALSE,
isLogged = isLogged)
})
})
session$sendCustomMessage("close_dropDownDeViolin", "")
}
})
# Linear Regression Plot
output$deRegTotalUI <- renderUI({
topN <- input$deRegNRow * input$deRegNCol
p(as.character(topN))
})
observeEvent(input$closeDropDownDeReg, {
session$sendCustomMessage("close_dropDownDeReg", "")
})
observeEvent(list(input$dePlotReg, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == "" &&
!is.null(vals$counts)){
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
labelBy <- NULL
if (input$deVioLabel != "Rownames (Default)" &
!is.null(useAssay)) {
labelBy <- input$deRegLabel
}
# MAST style sanity check for whether logged or not
if (!is.null(useAssay)) {
x <- expData(vals$counts, useAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnReg <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnReg <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
} else {
isLogged <- TRUE
}
message(date(), " ... Updating DE regression plot for analysis: ",
input$deResSel)
output$deRegPlot <- renderPlot({
isolate({
plotDEGRegression(inSCE = vals$counts,
useResult = input$deResSel,
nrow = input$deRegNRow,
ncol = input$deRegNCol,
labelBy = labelBy,
check_sanity = FALSE,
isLogged = isLogged)
})
})
session$sendCustomMessage("close_dropDownDeReg", "")
}
})
# Heatmap
output$deHMSplitColUI <- renderUI({
otherAvail <- input$deHMcolData
selectInput("deHMSplitCol", "Split columns by", multiple = TRUE,
choices = c('condition', otherAvail),
selected = 'condition')
})
output$deHMSplitRowUI <- renderUI({
otherAvail <- input$deHMrowData
selectInput("deHMSplitRow", "Split rows by", multiple = TRUE,
choices = c('regulation', otherAvail),
selected = 'regulation')
})
observeEvent(input$closeDropDownDeHM, {
session$sendCustomMessage("close_dropDownDeHM", "")
})
observeEvent(list(input$dePlotHM, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == ""){
deHMMinExp1 <- handleEmptyInput(input$deHMMinExp1, "numeric", NULL)
deHMMaxExp2 <- handleEmptyInput(input$deHMMaxExp2, "numeric", NULL)
deHMMinExpPerc1 <- handleEmptyInput(input$deHMMinExpPerc1, "numeric", NULL)
deHMMaxExpPerc2 <- handleEmptyInput(input$deHMMaxExpPerc2, "numeric", NULL)
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
rowLabel <- FALSE
if (isTRUE(input$deHMShowRowLabel)) {
rowLabel <- TRUE
if (input$deHMrowLabel != "Rownames (Default)" &
!is.null(useAssay)) {
rowLabel <- input$deHMrowLabel
}
}
message(date(), " ... Updating DE heatmap for analysis: ",
input$deResSel)
output$deHeatmap <- renderPlot({
isolate({
plotDEGHeatmap(inSCE = vals$counts,
useResult = input$deResSel,
doLog = input$deHMDoLog,
onlyPos = input$deHMPosOnly,
log2fcThreshold = input$deHMFC,
fdrThreshold = input$deHMFDR,
rowDataName = input$deHMrowData,
colDataName = input$deHMcolData,
colSplitBy = input$deHMSplitCol,
rowSplitBy = input$deHMSplitRow,
rowLabel = rowLabel)
})
})
session$sendCustomMessage("close_dropDownDeHM", "")
}
})
#-----------------------------------------------------------------------------
# Page 5.2: Find Marker ####
#-----------------------------------------------------------------------------
observeEvent(input$fmMethod, {
if (!is.null(vals$counts)) {
if (is.null(input$fmMethod)) {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
} else if (input$fmMethod == "DESeq2") {
updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
} else {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
}
}
})
# findMarker RUN ####
observeEvent(input$runFM, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
fdrThreshold <- handleEmptyInput(input$fmFDR)
vals$counts <- runFindMarker(inSCE = vals$counts,
method = input$fmMethod,
useAssay = input$fmAssay,
cluster = input$fmCluster,
covariates = input$fmCovar,
fdrThreshold = fdrThreshold)
message(date(), " ... Updating find marker heatmap")
updateFMPlot()
callModule(
module = filterTableServer,
id = "filterfmResTable",
dataframe = getFindMarkerTopTable(vals$counts, log2fcThreshold = 0,
minClustExprPerc = 0,
maxCtrlExprPerc = 1,
minMeanExpr = 0, topN = NULL),
defaultFilterColumns = c("Log2_FC", "clusterExprPerc", "ControlExprPerc", "clusterAveExpr"),
defaultFilterOperators = c(">", ">=", "<=", ">="),
defaultFilterValues = c("0", "0", "1", "0"),
initialTopN = 100,
topText = "You can view the markers of each cluster in the table below. And you can apply customized filters to filter the table accordingly."
)
}
))
# findMarker ResultTable ####
# output$fmResTable <- DT::renderDataTable({
# if(!is.null(vals$counts) &&
# 'findMarker' %in% names(metadata(vals$counts))){
# fullTable <- metadata(vals$counts)$findMarker
# fullTable[,5] <- as.factor(fullTable[,5])
# fullTable
# }
# }, filter = "top", options = list(scrollX = TRUE))
observe({
if (!is.null(vals$counts) &&
!is.null(metadata(vals$counts)$findMarker)) {
shinyjs::enable("fmDownload")
} else {
shinyjs::disable("fmDownload")
}
})
output$fmDownload <- downloadHandler(
filename = function() {
paste0("findMarkerResult_", input$fmCluster, ".csv")
},
content = function(file) {
fullTable <- metadata(vals$counts)$findMarker
filteredTable <- fullTable[input$fmResTable_rows_all,]
utils::write.csv(filteredTable, file, row.names = FALSE)
}
)
# findMarker Heatmap ####
observeEvent(input$fmShowHMSetting, {
if (isTRUE(vals$fmHMshowHide)) {
shinyjs::hide("fmHMsettings")
updateActionButton(session, "fmShowHMSetting", label = "Show Settings")
vals$fmHMshowHide <- FALSE
} else {
shinyjs::show("fmHMsettings")
updateActionButton(session, "fmShowHMSetting", label = "Hide Settings")
vals$fmHMshowHide <- TRUE
}
})
observeEvent(input$fmUseTopN, {
if (!isTRUE(input$fmUseTopN)) {
shinyjs::disable("fmTopN")
} else {
shinyjs::enable("fmTopN")
}
})
observeEvent(input$closeDropDownFM, {
session$sendCustomMessage("close_dropDownFM", "")
})
observeEvent(input$plotFM, {
updateFMPlot()
session$sendCustomMessage("close_dropDownFM", "")
})
updateFMPlot <- function() {
if(!is.null(vals$counts) &&
'findMarker' %in% names(metadata(vals$counts))){
withBusyIndicatorServer("plotFM", {
message(paste0(date(), " ... Updating marker heatmap"))
if (isTRUE(input$fmUseTopN)
&& is.na(input$fmTopN)) {
stop("Top N marker must be a numeric non-empty value")
}
if (is.na(input$fmHMFC)) {
stop("Log2FC must be a numeric non-empty value!")
}
if (is.na(input$fmHMFDR)) {
stop("FDR must be a numeric non-empty value!")
}
if (!isTRUE(input$fmUseTopN)) {
topN <- NULL
} else {
topN <- input$fmTopN
}
if (input$fmHMFeatureDisplay != "Rownames (Default)") {
rowLabel <- input$fmHMFeatureDisplay
} else {
rowLabel <- TRUE
}
# Take value before rendering plot, so that the plot doesn't auto
# re-render while we tweak the parameter
output$fmHeatmap <- renderPlot({
isolate({
plotFindMarkerHeatmap(inSCE = vals$counts,
orderBy = input$fmHMOrder,
log2fcThreshold = input$fmHMFC,
topN = topN,
fdrThreshold = input$fmHMFDR,
decreasing = input$fmHMdec,
rowDataName = input$fmHMrowData,
colDataName = input$fmHMcolData,
minClustExprPerc = input$fmHMMinClustExprPerc,
maxCtrlExprPerc = input$fmHMMaxCtrlExprPerc,
minMeanExpr = input$fmHMMinMeanExpr,
rowLabel = rowLabel)
})
})
})
}
}
#-----------------------------------------------------------------------------
# Page 6: Pathway Activity Analysis
#-----------------------------------------------------------------------------
observeEvent(input$pathwayImportGS, {
showTab(inputId = "navbar",
target = "Import Gene Sets",
select = TRUE,
session = session)
})
#colData for grouping the data (optional for user)
observeEvent(input$pathway, {
if(!is.null(vals$counts)){
updateSelectInput(session, "pathwayPlotVar", choices = colnames(colData(vals$counts)))
}
})
#select geneset collection name for pathway analysis
#output$selectPathwayGeneLists <- renderUI({
# if (!is.null(vals$counts)){
# if (!is.null(metadata(vals$counts)$sctk$genesets)) {
# newGSchoices <- sctkListGeneSetCollections(vals$counts)
# selectizeInput("PathwayGeneLists", "Select Geneset Collection(s):",
# choices = newGSchoices, multiple = FALSE)
# }
# } else {
# HTML("<h5><span style='color:red'>Must import geneset data first!</span></h5></br>")
# }
#})
#Run algorithm
observeEvent(input$pathwayRun, withConsoleMsgRedirect(
msg = "Please wait while pathway analysis are being performed. See console log for progress.",
{
req(vals$counts)
if (input$PathwayGeneLists == "Import geneset before using") {
stop("Must import geneset first.", type = "error")
}
if (input$pathway == "VAM") {
vals$counts <- runVAM(inSCE = vals$counts,
useAssay = input$vamAssay,
geneSetCollectionName = input$PathwayGeneLists,
center = input$vamCenterParameter,
gamma = input$vamGammaParameter)
scoreSelect <- paste0("VAM_", input$PathwayGeneLists, "_CDF")
} else if (input$pathway == "GSVA") {
vals$counts <- runGSVA(inSCE = vals$counts,
useAssay = input$vamAssay,
geneSetCollectionName = input$PathwayGeneLists)
scoreSelect <- paste0("GSVA_", input$PathwayGeneLists, "_Scores")
}
updateAssayInputs()
updateReddimInputs()
availPathwayRes <- getPathwayResultNames(vals$counts)
firstGS <- colnames(reducedDim(vals$counts, scoreSelect))[1]
updateSelectizeInput(session, "pathwayRedDimNames",
choices = availPathwayRes, selected = scoreSelect)
updateSelectizeInput(session, "pathwayPlotGS",
choices = colnames(reducedDim(vals$counts, scoreSelect)),
selected = firstGS)
#plot results with default values intitially
output$pathwayPlot <- renderPlot({
isolate({
plotPathway(inSCE = vals$counts,
resultName = scoreSelect,
geneset = firstGS,
groupBy = input$pathwayPlotVar,
boxplot = input$pathwayPlotBoxplot,
violin = input$pathwayPlotViolinplot,
dots = input$pathwayPlotDots,
summary = input$pathwayPlotSummary)
})
})
}
))
observeEvent(input$pathwayRedDimNames, {
if (!is.null(vals$counts)) {
updateSelectizeInput(session, "pathwayPlotGS",
choices = colnames(reducedDim(vals$counts, input$pathwayRedDimNames)))
}
})
#plot results
observeEvent(input$pathwayPlot, {
output$pathwayPlot <- renderPlot({
isolate({
plotPathway(inSCE = vals$counts,
resultName = input$pathwayRedDimNames,
geneset = input$pathwayPlotGS,
groupBy = input$pathwayPlotVar,
boxplot = input$pathwayPlotBoxplot,
violin = input$pathwayPlotViolinplot,
dots = input$pathwayPlotDots,
summary = input$pathwayPlotSummary)
})
})
session$sendCustomMessage("close_dropDownPathway", "")
})
observeEvent(input$closeDropDownPathway,{
session$sendCustomMessage("close_dropDownPathway", "")
})
#disable downloadPathway button if the pathway data doesn't exist
#isVamResult <- reactive(is.null(vals$vamResults))
#isGsvaResult <- reactive(is.null(vals$gsvaResults))
#observe({
# if (isVamResult() && isGsvaResult()) {
# shinyjs::disable("downloadPathway")
# } else {
# shinyjs::enable("downloadPathway")
# }
#})
#download pathway results
#output$downloadPathway <- downloadHandler(
# filename = function() {
# paste("Pathway_results-", Sys.Date(), ".csv", sep = "")
# },
# content = function(file) {
# if(input$pathway == "VAM"){
# utils::write.csv(vals$vamResults, file)
# }
# else if (input$pathway == "GSVA"){
# utils::write.csv(vals$gsvaResults, file)
# }
# }
#)
#-----------------------------------------------------------------------------
# Page 6.2 : Enrichment Analysis - EnrichR ####
#-----------------------------------------------------------------------------
enrichRfile <- reactive(read.csv(input$enrFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote,
row.names = 1))
updateEnrichRAnalysisNames <- function(selected = NULL) {
if (is.null(selected)) {
selected <- input$enrAnalysisNameSel
}
allNames <- names(metadata(vals$counts)$sctk$runEnrichR)
updateSelectInput(session, "enrAnalysisNameSel",
label = "Select analysis name:",
choices = allNames,
selected = selected)
}
update_enrDEG <- reactive({
list(input$enrDEGSelect, input$enrDEGUpOnly, input$enrDEGlog2fc,
input$enrDEGFDR, input$enrDEGminMean1, input$enrDEGmaxMean2,
input$enrDEGminPerc1, input$enrDEGmaxPerc2)
})
observeEvent(ignoreInit = TRUE, update_enrDEG(), {
req(vals$counts)
req(input$enrDEGSelect)
degSelect <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
labelBy = NULL, onlyPos = input$enrDEGUpOnly,
log2fcThreshold = input$enrDEGlog2fc,
fdrThreshold = input$enrDEGFDR,
minGroup1MeanExp = input$enrDEGminMean1,
maxGroup2MeanExp = input$enrDEGmaxMean2,
minGroup1ExprPerc = input$enrDEGminPerc1,
maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
nGene <- length(degSelect)
output$enrDEGText <- renderUI(p(paste0("Selected ", nGene, " DEGs. Listed below.")))
output$enrDEGRes <- renderText({
isolate({
degSelect
})
})
})
#count_db <- reactive(length(dbs()))
observeEvent (input$enrichRun, withConsoleMsgRedirect(
msg = "Please wait while EnrichR is running. See console log for progress.",
{
req(vals$counts)
if (!internetConnection) {
stop("Internet connection failed.")
}
if (input$enrAnalysisNameSet == "" |
is.null(input$enrAnalysisNameSet)) {
stop("The analysis name has to be specified")
}
by <- "rownames"
if (input$geneListChoice == "deg") {
genes <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
labelBy = NULL, onlyPos = input$enrDEGUpOnly,
log2fcThreshold = input$enrDEGlog2fc,
fdrThreshold = input$enrDEGFDR,
minGroup1MeanExp = input$enrDEGminMean1,
maxGroup2MeanExp = input$enrDEGmaxMean2,
minGroup1ExprPerc = input$enrDEGminPerc1,
maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
} else if (input$geneListChoice == "selectGenes"){
genes <- input$enrichGenes
} else if (input$geneListChoice == "geneFile"){
req(input$enrFile)
genes <- rownames(enrichRfile())
message(date(), " ... Reading from file. The first three features are:")
message(date(), " ", paste(genes[seq(3)], collapse = ", "))
by <- input$enrFileBy
}
message(date(), " ... Performing GSEA with enrichR")
vals$counts <- runEnrichR(inSCE = vals$counts,
features = genes,
analysisName = input$enrAnalysisNameSet,
db = input$enrichDb,
by = by,
featureName = input$enrFeatureName)
updateEnrichRAnalysisNames(selected = input$enrAnalysisNameSet)
}
))
enrChangeDBShow <- reactive({
list(input$enrAnalysisNameSel,
input$enrichRun)
})
observeEvent(enrChangeDBShow(), {
req(input$enrAnalysisNameSel)
dbs <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
updateSelectizeInput(session, "enrDbShow", choices = dbs,
selected = input$enrDbShow)
})
enrResultSel <- reactive({
list(input$enrAnalysisNameSel,
input$enrDbShow,
input$enrichRun)
})
#create datatables
observeEvent(enrResultSel(), {
req(vals$counts)
req(input$enrAnalysisNameSel)
res <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$result
dbToShow <- input$enrDbShow
if (is.null(dbToShow)) {
dbToShow <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
}
res <- res[which(res[, 1] %in% dbToShow), ]
vals$enrichRes <- res
#tableToShow <- res[, c(1:10)] %>%
# mutate(Database_selected =
# paste0("<a href='", res[, 11],
# "' target='_blank'>",
# res[, 1], "</a>"))
tableToShow <- res
output$enrDataTable <- DT::renderDataTable({
DT::datatable({
tableToShow
},
escape = FALSE,
options = list(scrollX = TRUE, pageLength = 20),
rownames = FALSE)
})
})
#disable the downloadEnrichR button if the result doesn't exist
isResult <- reactive(is.null(vals$enrichRes))
observe({
if (isResult()) {
shinyjs::disable("downloadEnrichR")
} else {
shinyjs::enable("downloadEnrichR")
}
})
output$downloadEnrichR <- downloadHandler(
filename = function() {
paste0("SCTK_enrichR_results_", input$enrAnalysisNameSel, "_",
Sys.Date(), ".csv")
},
content = function(file) {
utils::write.csv(vals$enrichRes, file, row.names = FALSE)
},
contentType = "text/csv"
)
#-----------------------------------------------------------------------------
# Page 7: Subsampling
#-----------------------------------------------------------------------------
#Run subsampling analysis
observeEvent(input$runSubsampleDepth, withConsoleMsgRedirect(
msg = "Please wait while subsampler is being computed. See console log for progress.",
{
req(vals$counts)
if(is.na(input$minCount)){
stop("Minimum readcount must be a non-empty numeric value!")
}
if(is.na(input$minCells)){
stop("Minimum number of cells must be a non-empty numeric value!")
}
if(is.na(input$iterations)){
stop("Number of bootstrap iterations must be a non-empty numeric value!")
}
vals$subDepth <- downSampleDepth(originalData = vals$counts,
useAssay = input$depthAssay,
minCount = input$minCount,
minCells = input$minCells,
maxDepth = 10 ^ input$maxDepth,
realLabels = input$selectReadDepthCondition,
depthResolution = input$depthResolution,
iterations = input$iterations)
output$depthDone <- renderPlot({
plot(apply(vals$subDepth[, , 1], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of detected genes",
main = "Number of dected genes by sequencing depth")
lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$minEffectDone <- renderPlot({
plot(apply(vals$subDepth[, , 2], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Average significant effect size",
ylim = c(0, 2))
lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$sigNumDone <- renderPlot({
plot(apply(vals$subDepth[, , 3], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of significantly DiffEx genes")
lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
}
))
observeEvent(input$runSubsampleCells, withConsoleMsgRedirect(
msg = "Please wait while resampler is being computed. See console log for progress.",
{
req(vals$counts)
if (is.na(input$minCellNum) ||
is.na(input$maxCellNum) ||
is.na(input$iterations) ||
is.na(input$totalReads) ||
is.na(input$minCount) ||
is.na(input$minCells) ||
is.na(input$depthResolution)) {
stop("One or more parameter values are empty!")
}
if (input$useReadCount) {
vals$subCells <- downSampleCells(originalData = vals$counts,
useAssay = input$cellsAssay,
realLabels = input$selectCellNumCondition,
totalReads = sum(SummarizedExperiment::assay(vals$counts, input$cellsAssay)),
minCellnum = input$minCellNum,
maxCellnum = input$maxCellNum,
minCountDetec = input$minCount,
minCellsDetec = input$minCells,
depthResolution = input$depthResolution,
iterations = input$iterations)
} else {
vals$subCells <- downSampleCells(originalData = vals$counts,
useAssay = input$cellsAssay,
realLabels = input$selectCellNumCondition,
totalReads = input$totalReads,
minCellnum = input$minCellNum,
maxCellnum = input$maxCellNum,
minCountDetec = input$minCount,
minCellsDetec = input$minCells,
depthResolution = input$depthResolution,
iterations = input$iterations)
}
output$cellsDone <- renderPlot({
plot(apply(vals$subCells[, , 1], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of virtual cells", ylab = "Number of detected genes",
main = "Number of dected genes by cell number")
lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$minEffectCells <- renderPlot({
plot(apply(vals$subCells[, , 2], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of virtual cells", ylab = "Average significant effect size",
ylim = c(0, 2))
lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$sigNumCells <- renderPlot({
plot(apply(vals$subCells[, , 3], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of vitual cells", ylab = "Number of significantly DiffEx genes")
lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
}
))
#Run differential power analysis
observeEvent(input$runSnapshot, withConsoleMsgRedirect(
msg = "Please wait while resampling snapshot is being computed. See console log for progress.",
{
req(vals$counts)
if (is.na(input$numCellsSnap) ||
is.na(input$numReadsSnap) ||
is.na(input$iterationsSnap)) {
stop("One or more parameter values are empty!")
}
vals$snapshot <- iterateSimulations(originalData = vals$counts,
useAssay = input$snapshotAssay,
realLabels = input$selectSnapshotCondition,
totalReads = input$numReadsSnap,
cells = input$numCellsSnap,
iterations = input$iterationsSnap)
vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapshotCondition])
output$Snaplot <- renderPlot({
plot(apply(vals$snapshot, 1, function(x){sum(x <= 0.05) / length(x)}) ~ vals$effectSizes,
xlab = "Cohen's d effect size", ylab = "Detection power", lwd = 4, main = "Power to detect diffex by effect size")
})
}
))
#-----------------------------------------------------------------------------
# Page 8: Seurat Workflow
#-----------------------------------------------------------------------------
#Perform normalization
observeEvent(input$normalize_button, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Normalizing Data"))
vals$counts <- runSeuratNormalizeData(inSCE = vals$counts,
useAssay = input$seuratSelectNormalizationAssay,
normAssayName = "seuratNormData",
normalizationMethod = input$normalization_method,
scaleFactor = as.numeric(input$scale_factor))
metadata(vals$counts)$sctk$seuratUseAssay <- input$seuratSelectNormalizationAssay
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts)
updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
updateAssayInputs()
message(paste0(date(), " ... Normalization Complete"))
}))
# #Perform scaling
# observeEvent(input$scale_button, withConsoleMsgRedirect ({
# #shows the notification spinner and console log
# .loadOpen ("Please wait while data is being scaled. See console log for progress.")
#
# req(vals$counts)
# message(paste0(date(), " ... Scaling Data"))
# vals$counts <- runSeuratScaleData(inSCE = vals$counts,
# useAssay = "seuratNormData",
# scaledAssayName = "seuratScaledData",
# #model = input$model.use,
# scale = input$do.scale,
# center = input$do.center,
# scaleMax = input$scale.max)
#
# vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE)
#
# updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
# shinyjs::enable(selector = "div[value='Dimensionality Reduction']")
# S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
# shinyjs::hide(
# selector = "div[value='Downstream Analysis']")
# message(paste0(date(), " ... Scaling Complete"))
#
# .loadClose() #close the notification spinner and console log
# }))
#Find HVG
observeEvent(input$find_hvg_button, withConsoleMsgRedirect(
msg = "Please wait while high variable genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding High Variable Genes"))
if(input$hvg_method == "vst" || packageVersion(pkg = "SeuratObject") >= 5.0){
vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
useAssay = metadata(vals$counts)$sctk$seuratUseAssay,
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features))
}
else{
vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
useAssay = "seuratNormData",
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features))
}
vals$counts <- setTopHVG(inSCE = vals$counts,
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features),
featureSubsetName = "featureSubset")
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, varFeatures = FALSE)
message(paste0(date(), " ... Plotting HVG"))
output$plot_hvg <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratHVG(vals$counts, input$hvg_no_features_view))
})
})
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... Finding HVG Complete"))
}
))
#Display highly variable genes
output$hvg_output <- renderText({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$obj)) {
if(packageVersion(pkg = "SeuratObject") >= 5.0){
if (length(vals$counts@metadata$seurat$obj$RNA$"var.features") > 0) {
isolate({
singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
})
}
}
else{
if (length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) > 0) {
isolate({
singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
})
}
}
}
})
#Run PCA
observeEvent(input$run_pca_button, withConsoleMsgRedirect(
msg = "Please wait while PCA is being computed. See console log for progress.",
{
req(vals$counts)
#remove tabs if not generated
removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
message(paste0(date(), " ... Running PCA"))
# For the commented line below:
# `useFeatureSubset`, in any functions that use it, goes to util function
# `.parseUseFeatureSubset()` which does a check for rownames(inSCE). Thus
# incompatible with Seurat's "_"-to-"-" change. But in `runSeuratPCA/ICA`,
# we automatically detect seurat HVG from the object when `useFeatureSubset
# = NULL`, so no need to specify this now.
vals$counts <- runSeuratPCA(inSCE = vals$counts,
useAssay = "seuratNormData",
reducedDimName = "seuratPCA",
#useFeatureSubset = getSeuratVariableFeatures(vals$counts),
nPCs = input$pca_no_components,
seed = input$seed_PCA,
scale = TRUE)
vals$counts@metadata$seurat$count_pc <- dim(convertSCEToSeurat(vals$counts)[["pca"]])[2]
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca")
)
), select = TRUE)
message(paste0(date(), " ... Plotting PCA"))
output$plot_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "pca",
showLegend = FALSE))
})
})
if (input$pca_compute_elbow) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
panel(
heading = "Elbow Plot",
plotlyOutput(outputId = "plot_elbow_pca")
)
))
message(paste0(date(), " ... Generating Elbow Plot"))
updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))
output$plot_elbow_pca <- renderPlotly({
isolate({
plotSeuratElbow(inSCE = vals$counts,
significantPC = singleCellTK:::.computeSignificantPC(vals$counts))
})
})
output$pca_significant_pc_output <- renderText({
isolate({
paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", singleCellTK:::.computeSignificantPC(vals$counts)," </span> </p> <hr>")
})
})
}
if (input$pca_compute_jackstraw) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
plotlyOutput(outputId = "plot_jackstraw_pca")
)
))
message(paste0(date(), " ... Generating JackStraw Plot"))
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally
dims = input$pca_no_components)
output$plot_jackstraw_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratJackStraw(inSCE = vals$counts,
dims = input$pca_no_components))
})
})
}
if (input$pca_compute_heatmap) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(4, dropdown(
fluidRow(
column(12,
fluidRow(actionBttn(inputId = "closeDropDownSeuratHM", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionBttn(
inputId = "plot_heatmap_pca_button",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
),
inputId = "dropDownSeuratHM",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(7, fluidRow(h6("Heatmaps of the top features correlated with each component"), align="center"))
)
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
)
)
))
message(paste0(date(), " ... Generating Heatmaps"))
vals$counts@metadata$seurat$heatmap_pca <- runSeuratHeatmap(inSCE = vals$counts,
useAssay = "seuratNormData",
useReduction = "pca",
dims = input$pca_no_components,
nfeatures = input$pca_compute_heatmap_nfeatures,
combine = FALSE,
fast = FALSE)
output$plot_heatmap_pca <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
dims = input$pca_no_components,
ncol = 2,
labels = c("PC1", "PC2", "PC3", "PC4"))
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
}
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
#Enable/Disable PCA plot panels not selected for computation (ElbowPlot, JackStraw or Heatmap)
shinyjs::enable(
selector = ".seurat_pca_plots a[data-value='PCA Plot']")
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='Elbow Plot']",
condition = input$pca_compute_elbow)
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='JackStraw Plot']",
condition = input$pca_compute_jackstraw)
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='Heatmap Plot']",
condition = input$pca_compute_heatmap)
shinyjs::enable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".seurat_pca_plots")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... PCA Complete"))
}))
observeEvent(input$closeDropDownSeuratHM,{
session$sendCustomMessage("close_dropDownSeuratHM", "")
})
#Run ICA
observeEvent(input$run_ica_button, withConsoleMsgRedirect(
msg = "Please wait while ICA is being computed. See console log for progress.",
{
req(vals$counts)
#remove tabs if not generated
removeTab(inputId = "seuratICAPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratICAPlotTabset", target = "Heatmap Plot")
message(paste0(date(), " ... Running ICA"))
vals$counts <- runSeuratICA(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally with scale = TRUE
nics = input$ica_no_components,
seed = input$seed_ICA,
scale = TRUE)
vals$counts@metadata$seurat$count_ic <- dim(convertSCEToSeurat(vals$counts)[["ica"]])[2]
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "ICA Plot",
panel(heading = "ICA Plot",
plotlyOutput(outputId = "plot_ica")
)
), select = TRUE)
message(paste0(date(), " ... Plotting ICA"))
output$plot_ica <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "ica",
showLegend = FALSE))
})
})
if (input$ica_compute_heatmap) {
appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_ica", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_ica", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionButton(inputId = "plot_heatmap_ica_button", "Plot")
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_ica"), options = list(maxWidth = 700))
)
)
))
message(paste0(date(), " ... Generating Heatmaps"))
vals$counts@metadata$seurat$heatmap_ica <- runSeuratHeatmap(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally
useReduction = "ica",
dims = input$ica_no_components,
nfeatures = input$ica_compute_heatmap_nfeatures,
combine = FALSE,
fast = FALSE)
output$plot_heatmap_ica <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
dims = input$ica_no_components,
ncol = 2,
labels = c("IC1", "IC2", "IC3", "IC4"))
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_ica", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_ic, "IC"))
}
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
#Enable/Disable ICA plot panels not selected for computation (Heatmap)
shinyjs::enable(
selector = ".seurat_ica_plots a[data-value='ICA Plot']")
shinyjs::toggleState(
selector = ".seurat_ica_plots a[data-value='Heatmap Plot']",
condition = input$ica_compute_heatmap)
shinyjs::enable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".seurat_ica_plots")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... ICA Complete"))
}))
#Find clusters
observeEvent(input$find_clusters_button, withConsoleMsgRedirect(
msg = "Please wait while clusters are being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToCluster = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_clustering_method]]
}
else
pathToCluster = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_clustering_method]]
if(!is.null(pathToCluster)){
#Remove plot tabs if generated before
removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
message(paste0(date(), " ... Clustering Dataset"))
vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
useReduction = input$reduction_clustering_method,
dims = input$pca_significant_pc_counter,
algorithm = input$algorithm.use,
groupSingletons = input$group.singletons,
resolution = input$resolution_clustering)
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
message(paste0(date(), " ... Finding Clusters Complete"))
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToUMAP = vals$counts@metadata$seurat$obj$"reductions"[["umap"]]
}
else
pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[["umap"]]
if(!is.null(pathToUMAP)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotlyOutput(outputId = "plot_umap_clustering")
)
), select = TRUE)
message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
output$plot_umap_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "umap",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='UMAP Plot']",
condition = !is.null(pathToUMAP))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToTSNE = vals$counts@metadata$seurat$obj$"reductions"[["tsne"]]
}
else
pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[["tsne"]]
if(!is.null(pathToTSNE)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotlyOutput(outputId = "plot_tsne_clustering")
)
), select = TRUE)
message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
output$plot_tsne_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "tsne",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='tSNE Plot']",
condition = !is.null(pathToTSNE))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToPCA = vals$counts@metadata$seurat$obj$"reductions"[["pca"]]
}
else
pathToPCA = vals$counts@metadata$seurat$obj@"reductions"[["pca"]]
if(!is.null(pathToPCA)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca_clustering")
)
)
)
message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
output$plot_pca_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "pca",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='PCA Plot']",
condition = !is.null(pathToPCA))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToICA = vals$counts@metadata$seurat$obj$"reductions"[["ica"]]
}
else
pathToICA = vals$counts@metadata$seurat$obj@"reductions"[["ica"]]
if(!is.null(pathToICA)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "ICA Plot",
panel(heading = "ICA Plot",
plotlyOutput(outputId = "plot_ica_clustering")
)
))
message(paste0(date(), " ... Re-generating ICA Plot with Cluster Labels"))
output$plot_ica_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "ica",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='ICA Plot']",
condition = !is.null(pathToICA))
}
shinyjs::show(selector = ".seurat_clustering_plots")
#enable find marker selection
shinyjs::enable(
selector = "#SeuratUI > div[value='Find Markers']")
#update colData names
updateColDataNames()
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
#populate updated colData items for findMarkers tab
updateSelectInput(session = session,
inputId = "seuratFindMarkerSelectPhenotype",
choices = colnames(colData(vals$counts)),
selected = paste0(
"Seurat_",
input$algorithm.use,
"_Resolution",
input$resolution_clustering))
#populate reducDim objects from seuratObject for findMarkers tab
updateSelectInput(session = session,
inputId = "seuratFindMarkerReductionMethod",
choices = Seurat::Reductions(convertSCEToSeurat(vals$counts)))
}
else{
showNotification(paste0("'", input$reduction_clustering_method, "' reduction not found in input object"))
}
}))
observeEvent(input$seuratFindMarkerSelectPhenotype, {
if(!is.null(vals$counts)){
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup1",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
)
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup2",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
)
}
})
observeEvent(input$seuratFindMarkerGroup1, {
if(!is.null(vals$counts)){
matchedIndex <- match(input$seuratFindMarkerGroup1, unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]]))
if(!is.na(matchedIndex)){
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup2",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])[-matchedIndex]
)
}
}
})
observeEvent(input$seuratFindMarkerRun, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding Marker Genes"))
if(input$seuratFindMarkerType == "markerAll"){
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
allGroup = input$seuratFindMarkerSelectPhenotype,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
else{
indices1 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup1, arr.ind = TRUE)
indices2 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup2, arr.ind = TRUE)
cells1 <- colnames(vals$counts)[indices1]
cells2 <- colnames(vals$counts)[indices2]
if(input$seuratFindMarkerType == "markerConserved"){
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
cells1 = cells1,
cells2 = cells2,
group1 = input$seuratFindMarkerGroup1,
group2 = input$seuratFindMarkerGroup2,
conserved = TRUE,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
else{
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
cells1 = cells1,
cells2 = cells2,
group1 = input$seuratFindMarkerGroup1,
group2 = input$seuratFindMarkerGroup2,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
}
shinyjs::show(selector = ".seurat_findmarker_table")
shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
shinyjs::show(selector = ".seurat_findmarker_plots")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
#df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
df <- metadata(vals$counts)$seuratMarkers
seuratObject <- convertSCEToSeurat(vals$counts, normAssay = "seuratNormData")
indices <- list()
cells <- list()
groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
for(i in seq(length(groups))){
indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
cells[[i]] <- colnames(vals$counts)[indices[[i]]]
cells[[i]] <- lapply(
X = cells[[i]],
FUN = function(t) gsub(
pattern = "_",
replacement = "-",
x = t,
fixed = TRUE)
)
if(seurat.version >= 5.0){
Idents(seuratObject, cells = unlist(cells[[i]])) <- groups[i]
}
else{
Idents(seuratObject, cells = cells[[i]]) <- groups[i]
}
}
showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "Ridge Plot")
shinyjs::show(selector = ".seurat_findmarker_plots")
# Output the heatmap
colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
top10markers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=10)
# Subset seuratObject to contain only cells available in selected clusters
if(input$seuratFindMarkerType != "markerAll"){
subsetIdents <- c(unique(top10markers$cluster1), unique(top10markers$cluster2))
subsetIdents <- subsetIdents[subsetIdents!="all"]
seuratObject <- subset(seuratObject, idents = subsetIdents)
}
seuratObject <- Seurat::ScaleData(seuratObject, features = top10markers$gene.id)
# Plot heatmap
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
DoHeatmap(seuratObject, features = top10markers$gene.id)
})
})
# output$findMarkerHeatmapPlotFullTopText <- renderUI({
# h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
# })
message(paste0(date(), " ... Find Markers Complete"))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-seurat", parent = session,
de = TRUE, fm = TRUE, pa = TRUE)
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerRidgePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerFeaturePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerDotPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "findMarkerHeatmapPlot")
)
)
)
)
)
)
#singleCellTK:::.exportMetaSlot(vals$counts, "seuratMarkers")
orderByLFCMarkers <- metadata(vals$counts)$seuratMarkers
orderByLFCMarkers <- orderByLFCMarkers[order(-orderByLFCMarkers$avg_log2FC), ]
vals$fts <- callModule(
module = filterTableServer,
id = "filterSeuratFindMarker",
dataframe = orderByLFCMarkers,
defaultFilterColumns = c("p_val_adj"),
defaultFilterOperators = c("<="),
defaultFilterValues = c("0.05"),
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
# vals$fts <- callModule(
# module = filterTableServer,
# id = "filterSeuratFindMarker",
# dataframe = orderByLFCMarkers
# )
}))
observeEvent(input$findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
msg = "Please wait while heatmap is being plotted. See console log for progress.",
{
##df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
df <- metadata(vals$counts)$seuratMarkers
seuratObject <- convertSCEToSeurat(vals$counts, normAssay = "seuratNormData")
indices <- list()
cells <- list()
groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
for(i in seq(length(groups))){
indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
cells[[i]] <- colnames(vals$counts)[indices[[i]]]
cells[[i]] <- lapply(
X = cells[[i]],
FUN = function(t) gsub(
pattern = "_",
replacement = "-",
x = t,
fixed = TRUE)
)
if(seurat.version >= 5.0){
cells[[i]] = unlist(cells[[i]])
}
Idents(seuratObject, cells = cells[[i]]) <- groups[i]
}
colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
topMarkers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=input$findMarkerHeatmapPlotFullNumeric)
#topMarkers <- data.frame(df %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, avg_log2FC))
# if(nrow(topMarkers) > (input$findMarkerHeatmapPlotFullNumeric * length(groups))){
# topMarkers <- data.frame(topMarkers %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, -p_val_adj))
# }
# Subset seuratObject to contain only cells available in selected clusters
if(input$seuratFindMarkerType != "markerAll"){
subsetIdents <- c(unique(topMarkers$cluster1), unique(topMarkers$cluster2))
subsetIdents <- subsetIdents[subsetIdents!="all"]
seuratObject <- subset(seuratObject, idents = subsetIdents)
}
seuratObject <- Seurat::ScaleData(seuratObject, features = topMarkers$gene.id)
# Plot heatmap
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
DoHeatmap(seuratObject, features = topMarkers$gene.id)
})
})
}))
observe({
req(vals$fts$data)
req(vals$fts$selectedRows)
df <- vals$fts$data[vals$fts$selectedRows, ]
output$findMarkerRidgePlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "ridge",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE
)
})
output$findMarkerViolinPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "violin",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE
)
})
output$findMarkerFeaturePlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "feature",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE,
useReduction = input$seuratFeatureUseReduction
)
})
output$findMarkerDotPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "dot",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype
)
})
output$findMarkerHeatmapPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "heatmap",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype
)
})
})
# observe({
# req(vals$fts$data)
# df <- vals$fts$data
# output$findMarkerHeatmapPlotFull <- renderPlot({
# plotSeuratGenes(
# inSCE = vals$counts,
# scaledAssayName = "seuratScaledData",
# plotType = "heatmap",
# features = df$gene_id,
# groupVariable = input$seuratFindMarkerSelectPhenotype
# )
# })
# })
#Update PCA/ICA message in clustering tab
output$display_message_clustering <- renderText({
if(input$reduction_clustering_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Run tSNE
observeEvent(input$run_tsne_button, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToTSNE = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_tsne_method]]
}
else
pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_tsne_method]]
if(!is.null(pathToTSNE)){
message(paste0(date(), " ... Running tSNE"))
vals$counts <- runSeuratTSNE(inSCE = vals$counts,
useReduction = input$reduction_tsne_method,
reducedDimName = "seuratTSNE",
dims = input$pca_significant_pc_counter,
perplexity = input$perplexity_tsne,
seed = input$seed_TSNE)
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)
message(paste0(date(), " ... Plotting tSNE"))
output$plot_tsne <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "tsne",
showLegend = FALSE))
})
})
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... tSNE Complete"))
}
else{
showNotification(paste0("'", input$reduction_tsne_method, "' reduction not found in input object"))
}
}))
#Update PCA/ICA message in tSNE tab
output$display_message_tsne <- renderText({
if(input$reduction_tsne_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Run UMAP
observeEvent(input$run_umap_button, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToUMAP = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_umap_method]]
}
else
pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_umap_method]]
if(!is.null(pathToUMAP)){
message(paste0(date(), " ... Running UMAP"))
vals$counts <- runSeuratUMAP(inSCE = vals$counts,
useReduction = input$reduction_umap_method,
reducedDimName = "seuratUMAP",
dims = input$pca_significant_pc_counter,
minDist = input$min_dist_umap,
nNeighbors = input$n_neighbors_umap,
spread = input$spread_umap,
seed = input$seed_UMAP)
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)
message(paste0(date(), " ... Plotting UMAP"))
output$plot_umap <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "umap",
showLegend = FALSE))
})
})
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... UMAP Complete"))
}
else{
showNotification(paste0("'", input$reduction_umap_method, "' reduction not found in input object"))
}
}))
#Update PCA/ICA message in UMAP tab
output$display_message_umap <- renderText({
if(input$reduction_umap_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{ #ICA to do
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Update pca significant slider maximum value with total number of computed principal components
observe({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$count_pc)) {
updateSliderInput(session = session, inputId = "pca_significant_pc_counter", max = vals$counts@metadata$seurat$count_pc)
}
})
#Update ica significant slider maximum value with total number of computed independent components
observe({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$count_ic)) {
updateNumericInput(session = session, inputId = "ica_significant_ic_counter", max = vals$counts@metadata$seurat$count_ic)
}
})
#Update tsne, umap and clustering selected number of principal components input
observe({
if (input$reduction_umap_method == "pca") {
updateTextInput(session = session, inputId = "reduction_umap_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_umap_method == "ica") {
updateTextInput(session = session, inputId = "reduction_umap_count", value = vals$counts@metadata$seurat$count_ic)
}
if (input$reduction_clustering_method == "pca") {
updateTextInput(session = session, inputId = "reduction_clustering_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_clustering_method == "ica") {
updateTextInput(session = session, inputId = "reduction_clustering_count", value = vals$counts@metadata$seurat$count_ic)
}
if (input$reduction_tsne_method == "pca") {
updateTextInput(session = session, inputId = "reduction_tsne_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_tsne_method == "ica") {
updateTextInput(session = session, inputId = "reduction_tsne_count", value = vals$counts@metadata$seurat$count_ic)
}
})
#Customize heatmap (pca) with selected options
observeEvent(input$plot_heatmap_pca_button, {
if (!is.null(input$picker_dimheatmap_components_pca)) {
output$plot_heatmap_pca <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
dims = length(input$picker_dimheatmap_components_pca),
ncol = input$slider_dimheatmap_pca,
labels = input$picker_dimheatmap_components_pca)
})
})
}
session$sendCustomMessage("close_dropDownSeuratHM", "")
})
#Customize heatmap (ica) with selected options
observeEvent(input$plot_heatmap_ica_button, {
if (!is.null(input$picker_dimheatmap_components_ica)) {
output$plot_heatmap_ica <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
dims = length(input$picker_dimheatmap_components_ica),
ncol = input$slider_dimheatmap_ica,
labels = input$picker_dimheatmap_components_ica)
})
})
}
})
#Disable Seurat tabs & reset collapse panel tabs
observe({
if(!is.null(vals$counts)){
#If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
shinyjs::enable(
selector = "#SeuratUI > div[value='Normalize Data']")
shinyjs::enable(
selector = "#ScanpyUI > div[value='Normalize Data']")
#Proceed only if sce object has metadata slot
if(!is.null(vals$counts@metadata)){
if(packageVersion(pkg = "SeuratObject") >= 5.0){
#Proceed only if sce object has seurat object stored in metadata slot
if(!is.null(vals$counts@metadata$seurat$obj)){
#If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
if(length(vals$counts@metadata$seurat$obj$RNA$"var.features") <= 0){
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
}
#Proceed if reduction slot is present in seurat object in metadata slot
if("reductions" %in% names(vals$counts@metadata$seurat$obj)){
#If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
if(is.null(vals$counts@metadata$seurat$obj$reductions$pca)
&& is.null(vals$counts@metadata$seurat$obj$reductions$ica)){
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
}
#If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
if(is.null(vals$counts@metadata$seurat$obj$reductions$tsne)
&& is.null(vals$counts@metadata$seurat$obj$reductions$umap)){
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
}
#If seurat cluster information removed from sce object, reset Clustering tab
if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj$meta.data)){
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
}
}
}
}
else{
#Proceed only if sce object has seurat object stored in metadata slot
if(!is.null(vals$counts@metadata$seurat$obj)){
# #If seuratScaledData has been removed from sce object, reset Scale Data tab and reset/lock its next tab
# if(!"seuratScaledData" %in% expDataNames(vals$counts)){
# updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "primary"))
# updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
# shinyjs::disable(selector = "div[value='Dimensionality Reduction']")
# }
#If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
if(length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) <= 0){
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
}
#Proceed if reduction slot is present in seurat object in metadata slot
if("reductions" %in% slotNames(vals$counts@metadata$seurat$obj)){
#If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["pca"]])
&& is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["ica"]])){
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
}
#If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["tsne"]])
&& is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["umap"]])){
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
}
#If seurat cluster information removed from sce object, reset Clustering tab
if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj@meta.data)){
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
}
}
}
}
}
}
else{
#If no data uploaded in data tab, disable all tabs and plots.
#Disable tabs
shinyjs::disable(
selector = "#SeuratUI > div[value='Normalize Data']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Normalize Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Highly Variable Genes']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Highly Variable Genes']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Scale Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Dimensionality Reduction']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
shinyjs::disable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='2D-Embedding']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Clustering']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Clustering']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Scale Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Find Markers']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Find Markers']")
#Disable plots inside PCA subtab
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='PCA Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='Elbow Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='JackStraw Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='Heatmap Plot']")
#Disable plots inside ICA subtab
shinyjs::disable(
selector = ".seurat_ica_plots a[data-value='ICA Plot']")
shinyjs::disable(
selector = ".seurat_ica_plots a[data-value='Heatmap Plot']")
#Disabled plots inside Clustering tab
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='PCA Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='ICA Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='tSNE Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='UMAP Plot']")
}
})
#-----------------------------------------------------------------------------
# Page: Column Annotation (colData) ####
#-----------------------------------------------------------------------------
#populate colData from sce object when uploaded
observe({
if(!is.null(vals$counts)){
if(!is.null(colData(vals$counts))){
vals$columnAnnotation <- as.data.frame(colData(vals$counts))
}
}
})
#import colData from local storage
observeEvent(input$importDataButton_colData, {
withBusyIndicatorServer("importDataButton_colData",{
if(!is.null(input$uploadFile_colData)){
temp <- read.csv(input$uploadFile_colData$datapath, header = TRUE,sep = ",")
if(nrow(colData(vals$counts)) == nrow(temp)){
if(input$editorChoiceRadio_colData == "replace"){
vals$columnAnnotation <- temp
}
else{
x <- as.data.frame(colData(vals$counts))
y <- as.data.frame(temp)
commonCols <- intersect(colnames(x), colnames(y))
x[, commonCols] <- y[,commonCols]
y[, commonCols] <- NULL
vals$columnAnnotation <- cbind(x, y)
}
}
else{
showNotification("Number of rows of the assay and the input colData must be equal", type = "error")
}
}
else{
showNotification("No file selected to upload", type = "error")
}
})
#Render a warning message if there are unsaved changes to colData
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#Render table with colData
output$outputColumnAnnotationTable_colData <- renderUI({
output$colOutTable <- DT::renderDataTable({
DT::datatable(vals$columnAnnotation,
editable = 'cell',
options = list(pageLength = 5,
scrollX = TRUE))
})
DT::dataTableOutput("colOutTable")
})
#create selectinput for selecting attribute with colnames from incoming dataset
#create selectinput for selecting attribute value
output$inputSelectAttribute_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttribute_colData",
label = "select attribute",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeDelete_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeDelete_colData",
label = "select attribute to delete",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting column to delete
output$inputSelectAttributeValue_colData <- renderUI({
if(!is.null(vals$columnAnnotation) &&
ncol(vals$columnAnnotation) > 0 &&
!is.null(input$inputSelectAttribute_colData) &&
input$inputSelectAttribute_colData %in% colnames(vals$columnAnnotation)){
selectInput("inputSelectAttributeValue_colData",
label = "select attribute value",
choices = vals$columnAnnotation[, input$inputSelectAttribute_colData])
}
})
#create selectinput for selecting merge_1 attribute
#create selectinput for selecting merge_2 attribute
output$inputSelectAttributeMerge1_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeMerge1_colData",
label = "select first column",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeMerge2_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeMerge2_colData",
label = "select second column",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting fill_1 attribute
#create selectinput for selecting fill_2 attribute
output$inputSelectAttributeFill1_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFill1_colData",
label = "select attribute column",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeFill2_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFill2_colData",
label = "select column to fill",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting attribute value for magic fill
output$inputSelectAttributeFillvalue_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFillvalue_colData",
label = "select attribute value",
choices = vals$columnAnnotation[, match(input$inputSelectAttributeFill1_colData,
colnames(vals$columnAnnotation))])
}
}
})
#update criteria parameter text input when attribute value selectinput is changed
observeEvent(input$inputSelectAttributeValue_colData, {
updateTextInput(session = session,
"inputCriteria_colData",
value = input$inputSelectAttributeValue_colData)
})
#create selectinput for selecting attribute for clean operation
output$inputSelectAttributeClean_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeClean_colData",
label = "select attribute column",
choices = colnames(vals$columnAnnotation))
}
}
})
#confirm create bin button
observeEvent(input$buttonConfirmBin_colData, {
#getting variables
selected_attribute <- input$inputSelectAttribute_colData
bin_name <- input$inputBinName_colData
selected_column_no <- match(selected_attribute, colnames(vals$columnAnnotation))
criteria_term <- input$inputCriteria_colData
operator_term <- input$inputOperator_colData
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operations
if (operator_term == "=")
{
df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
}
else if (operator_term == ">")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
}
else if (operator_term == "<")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
}
else if (operator_term == "<=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
}
else if (operator_term == ">=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm merge button
observeEvent(input$buttonConfirmMerge_colData, {
df <- vals$columnAnnotation
colname1 <- input$inputSelectAttributeMerge1_colData
colname2 <- input$inputSelectAttributeMerge2_colData
df <- unite_(df, col = colname1, c(colname1, colname2),
sep = input$inputSelectSeparatorMerge_colData)
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#fill column button
observeEvent(input$buttonConfirmFill_colData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operation
selected_attribute_1 <- input$inputSelectAttributeFill1_colData
selected_attribute_2 <- input$inputSelectAttributeFill2_colData
selected_column_no_1 <- match(selected_attribute_1, colnames(df))
selected_column_no_2 <- match(selected_attribute_2, colnames(df))
old_value <- input$inputSelectAttributeFillvalue_colData
new_value <- input$inputReplaceText_colData
for (i in 1:nrow(df))
{
if (df[i, selected_column_no_1] == old_value)
{
df[i, selected_column_no_2] <- new_value
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm clean button
observeEvent(input$buttonConfirmClean_colData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operation
selected_attribute <- input$inputSelectAttributeClean_colData
selected_column_no <- match(selected_attribute, colnames(df))
selected_choice <- input$inputRemovalOperation_colData
selected_choice_no <- match(selected_choice, c("remove alphabets",
"remove digits",
"remove spaces",
"remove symbols"))
if (selected_choice_no == 1)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 2)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 3)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 4)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#add new empty column button
observeEvent(input$buttonConfirmEmptyColumnName_colData, {
df <- vals$columnAnnotation
colname <- input$inputEmptyColumnName_colData
df$newcolumn <- input$inputDefaultValueAddColumn_colData
names(df)[ncol(df)] <- colname
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#delete column button
observeEvent(input$buttonConfirmDeleteColumn_colData,{
#getting variables
selected_attribute <- input$inputSelectAttributeDelete_colData
#get df from reactive input
df <- vals$columnAnnotation
#delete
df[[selected_attribute]] <- NULL
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#restore saved/original colData
observeEvent(input$buttonRestore_colData,{
vals$columnAnnotation <- as.data.frame(colData(vals$counts))
output$changesWarning_colData <- NULL
showNotification("Changes reverted back to last checkpoint.")
})
#save changes to colData
observeEvent(input$buttonSave_colData,{
colData(vals$counts) <- DataFrame(vals$columnAnnotation)
output$changesWarning_colData <- NULL
updateColDataNames()
showNotification("Changes saved successfully.")
})
#-----------------------------------------------------------------------------
# Page: Row Annotation (rowData) ####
#-----------------------------------------------------------------------------
#populate colData from sce object when uploaded
observe({
if(!is.null(vals$counts)){
if(!is.null(rowData(vals$counts))){
vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
}
}
})
#import rowData from local storage
observeEvent(input$importDataButton_rowData, {
withBusyIndicatorServer("importDataButton_rowData",{
if(!is.null(input$uploadFile_rowData)){
temp <- read.csv(input$uploadFile_rowData$datapath, header = TRUE,sep = ",")
if(nrow(rowData(vals$counts)) == nrow(temp)){
if(input$editorChoiceRadio_rowData == "replace"){
vals$rowAnnotation <- temp
}
else{
x <- as.data.frame(rowData(vals$counts))
y <- as.data.frame(temp)
commonCols <- intersect(colnames(x), colnames(y))
x[, commonCols] <- y[,commonCols]
y[, commonCols] <- NULL
vals$rowAnnotation <- cbind(x, y)
}
}
else{
showNotification("Number of rows of the assay and the input rowData must be equal", type = "error")
}
}
else{
showNotification("No file selected to upload", type = "error")
}
})
#Render a warning message if there are unsaved changes to rowData
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#Render table with rowData
output$outputColumnAnnotationTable_rowData <- renderUI({
output$rowOutTable <- DT::renderDataTable({
DT::datatable(vals$rowAnnotation,
editable = 'cell',
options = list(pageLength = 5,
scrollX = TRUE))
})
DT::dataTableOutput("rowOutTable")
})
#create selectinput for selecting attribute with colnames from incoming dataset
#create selectinput for selecting attribute value
output$inputSelectAttribute_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttribute_rowData",
label = "select attribute",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeDelete_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeDelete_rowData",
label = "select attribute to delete",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting column to delete
observeEvent(input$inputSelectAttribute_rowData, {
if(!is.null(vals$rowAnnotation) &&
ncol(vals$rowAnnotation) > 0 &&
!is.null(input$inputSelectAttribute_rowData) &&
input$inputSelectAttribute_rowData %in% colnames(vals$rowAnnotation)){
updateSelectizeInput(session, "inputSelectAttributeValue_rowData",
choices = vals$rowAnnotation[, input$inputSelectAttribute_rowData],
server = TRUE)
}
})
#create selectinput for selecting merge_1 attribute
#create selectinput for selecting merge_2 attribute
output$inputSelectAttributeMerge1_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeMerge1_rowData",
label = "select first column",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeMerge2_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeMerge2_rowData",
label = "select second column",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting fill_1 attribute
#create selectinput for selecting fill_2 attribute
output$inputSelectAttributeFill1_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeFill1_rowData",
label = "select attribute column",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeFill2_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeFill2_rowData",
label = "select column to fill",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting attribute value for magic fill
observeEvent(input$inputSelectAttributeFill1_rowData, {
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
updateSelectizeInput(session, "inputSelectAttributeFillvalue_rowData",
choices = vals$rowAnnotation[, match(input$inputSelectAttributeFill1_rowData,
colnames(vals$rowAnnotation))],
server = TRUE)
}
}
})
#update criteria parameter text input when attribute value selectinput is changed
observeEvent(input$inputSelectAttributeValue_rowData, {
updateTextInput(session = session,
"inputCriteria_rowData",
value = input$inputSelectAttributeValue_rowData)
})
#create selectinput for selecting attribute for clean operation
output$inputSelectAttributeClean_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeClean_rowData",
label = "select attribute column",
choices = colnames(vals$rowAnnotation))
}
}
})
#confirm create bin button
observeEvent(input$buttonConfirmBin_rowData, {
#getting variables
selected_attribute <- input$inputSelectAttribute_rowData
bin_name <- input$inputBinName_rowData
selected_column_no <- match(selected_attribute, colnames(vals$rowAnnotation))
criteria_term <- input$inputCriteria_rowData
operator_term <- input$inputOperator_rowData
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
if (operator_term == "=")
{
df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
}
else if (operator_term == ">")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
}
else if (operator_term == "<")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
}
else if (operator_term == "<=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
}
else if (operator_term == ">=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm merge button
observeEvent(input$buttonConfirmMerge_rowData, {
df <- vals$rowAnnotation
colname1 <- input$inputSelectAttributeMerge1_rowData
colname2 <- input$inputSelectAttributeMerge2_rowData
df <- unite_(df, col = colname1, c(colname1, colname2),
sep = input$inputSelectSeparatorMerge_rowData)
vals$rowAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#fill column button
observeEvent(input$buttonConfirmFill_rowData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
selected_attribute_1 <- input$inputSelectAttributeFill1_rowData
selected_attribute_2 <- input$inputSelectAttributeFill2_rowData
selected_column_no_1 <- match(selected_attribute_1, colnames(df))
selected_column_no_2 <- match(selected_attribute_2, colnames(df))
old_value <- input$inputSelectAttributeFillvalue_rowData
new_value <- input$inputReplaceText_rowData
for (i in 1:nrow(df))
{
if (df[i, selected_column_no_1] == old_value)
{
df[i, selected_column_no_2] <- new_value
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm clean button
observeEvent(input$buttonConfirmClean_rowData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
selected_attribute <- input$inputSelectAttributeClean_rowData
selected_column_no <- match(selected_attribute, colnames(df))
selected_choice <- input$inputRemovalOperation_rowData
selected_choice_no <- match(selected_choice, c("remove alphabets",
"remove digits",
"remove spaces",
"remove symbols"))
if (selected_choice_no == 1)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 2)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 3)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 4)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#add new empty column button
observeEvent(input$buttonConfirmEmptyColumnName_rowData, {
df <- vals$rowAnnotation
colname <- input$inputEmptyColumnName_rowData
df$newcolumn <- input$inputDefaultValueAddColumn_rowData
names(df)[ncol(df)] <- colname
vals$columnAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#delete column button
observeEvent(input$buttonConfirmDeleteColumn_rowData,{
#getting variables
selected_attribute <- input$inputSelectAttributeDelete_rowData
#get df from reactive input
df <- vals$rowAnnotation
#delete
df[[selected_attribute]] <- NULL
vals$rowAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#restore saved/original rowData
observeEvent(input$buttonRestore_rowData,{
vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
output$changesWarning_rowData <- NULL
showNotification("Changes reverted back to last checkpoint.")
})
#save changes to rowData
observeEvent(input$buttonSave_rowData,{
rowData(vals$counts) <- DataFrame(vals$rowAnnotation)
output$changesWarning_rowData <- NULL
updateFeatureAnnots()
showNotification("Changes saved successfully.")
})
#-----------------------------------------------------------------------------
# Page Download ####
#-----------------------------------------------------------------------------
output$exportFileName <- renderUI({
defaultName <- paste0("SCE-", strftime(Sys.time(), format = "%y%m%d_%H%M"))
if (input$exportChoice == "rds") {
extName <- ".rds"
} else if (input$exportChoice == "annData") {
extName <- ".h5ad"
}
tags$div(
div(style = "display: inline-block;vertical-align:top; width: 160px;",
textInput("exportPrefix", label = NULL,
value = defaultName, placeholder = "Required!",
width = '160px')),
div(
style = "display: inline-block;vertical-align:top; width: 50px;",
p(extName, style = "margin-top: 8px; margin-left: 2px; font-size: 16px;")
)
)
})
addPopover(session, 'exportAssayLabel', '', "The name of assay of interests that will be set as the primary matrix of the output AnnData.", 'right')
output$exportData <- downloadHandler(
filename = function() {
if (input$exportChoice == "rds") {
paste0(input$exportPrefix, ".rds")
}
else if (input$exportChoice == "annData"){
paste0(input$exportPrefix, ".h5ad")
}
},
content = function(file) {
if (input$exportChoice == "rds") {
saveRDS(vals$counts, file)
}
else if (input$exportChoice == "annData") {
zellkonverter::writeH5AD(sce = vals$counts,
file,
X_name = input$exportAssay)
}
}
)
##############################################################################
# Page: Cell Type Labeling ####
##############################################################################
output$ctLabelLevelUI <- renderUI({
if (input$ctLabelRef %in% c("hpca", "bpe", "dice", "immgen", "mouse")) {
selectInput("ctLabelLevel", "Labeling level:",
c("main", "fine", "ont"), "main")
} else {
disabled(
selectInput("ctLabelLevel", "Labeling level (not supported):",
choices = NULL, selected = NULL)
)
}
})
observeEvent(input$ctLabelRun, withConsoleMsgRedirect(
msg = "Please wait while cell types are being labeled. See console log for progress.",
{
req(vals$counts)
if (input$ctLabelBy == "Clusters") {
cluster <- input$ctLabelByCluster
if (is.null(cluster) || cluster == "") {
stop("Choose the clustering label for this condition!")
}
} else {
cluster <- NULL
}
vals$counts <- runSingleR(vals$counts,
useAssay = input$ctLabelAssay,
useBltinRef = input$ctLabelRef,
level = input$ctLabelLevel,
featureType = input$ctLabelFeatureType,
labelByCluster = cluster)
updateColDataNames()
message(date(), " ... SingleR finished")
}
))
##############################################################################
# Page: Scanpy Curated Workflow ####
##############################################################################
# Run Normalization
observeEvent(input$scanpy_normalize_button, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Normalizing Data"))
vals$counts <- runScanpyNormalizeData(inSCE = vals$counts,
useAssay = input$scanpySelectNormalizationAssay,
targetSum = input$scanpy_targetSum,
maxFraction = input$scanpy_maxFraction,
normAssayName = "scanpyNormData"
)
updateCollapse(session = session, "ScanpyUI", style = list("Normalize Data" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Highly Variable Genes']")
updateAssayInputs()
message(paste0(date(), " ... Normalization Complete"))
}))
# HVG
#Find HVG
observeEvent(input$scanpy_find_hvg_button, withConsoleMsgRedirect(
msg = "Please wait while high variable genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding High Variable Genes"))
useAssay <- NULL
maxDisp <- NULL
if(input$scanpy_maxDisp_Inf){
maxDisp <- Inf
}
else{
maxDisp <- input$scanpy_maxDisp
}
vals$counts <- runScanpyFindHVG(inSCE = vals$counts,
useAssay = "scanpyNormData",
method = input$scanpy_hvg_method,
hvgNumber = input$scanpy_hvg_no_features,
minMean = input$scanpy_minMean,
maxMean = input$scanpy_maxMean,
minDisp = input$scanpy_minDisp,
maxDisp = maxDisp)
message(paste0(date(), " ... Finding HVG Complete"))
updateCollapse(session = session, "ScanpyUI", style = list("Highly Variable Genes" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
message(paste0(date(), " ... Plotting HVG"))
output$scanpy_plot_hvg <- renderPlot({
isolate({
plotScanpyHVG(vals$counts)
})
})
message(paste0(date(), " ... Finding HVG Complete"))
}
))
# PCA
observeEvent(input$scanpy_run_pca_button, withConsoleMsgRedirect(
msg = "Please wait while PCA is being computed. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Running PCA"))
vals$counts <- runScanpyScaleData(inSCE = vals$counts,
useAssay = "scanpyNormData")
vals$counts <- runScanpyPCA(inSCE = vals$counts,
useAssay = "scanpyScaledData",
method = input$scanpy_pca_method,
nPCs = input$scanpy_pca_no_components,
reducedDimName = "scanpyPCA",
use_highly_variable = TRUE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Variance",
panel(heading = "PCA Variance",
plotOutput(outputId = "scanpy_plot_pca_variance")
)
), select = TRUE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotOutput(outputId = "scanpy_plot_pca")
)
), select = FALSE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Gene Ranking",
panel(heading = "PCA Gene Ranking",
plotOutput(outputId = "scanpy_plot_pca_gene_ranking")
)
), select = FALSE)
message(paste0(date(), " ... Plotting PCA"))
output$scanpy_plot_pca_variance <- renderPlot({
isolate({
plotScanpyPCAVariance(inSCE = vals$counts, log = TRUE)
})
})
output$scanpy_plot_pca_gene_ranking <- renderPlot({
isolate({
plotScanpyPCAGeneRanking(inSCE = vals$counts)
})
})
output$scanpy_plot_pca <- renderPlot({
isolate({
plotScanpyPCA(inSCE = vals$counts)
})
})
updateCollapse(session = session, "ScanpyUI", style = list("Dimensionality Reduction" = "success"))
shinyjs::enable(
selector = "#ScanpyUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".scanpy_pca_plots")
message(paste0(date(), " ... PCA Complete"))
}))
# TSNE
observeEvent(input$scanpy_run_tsne_button, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Running tSNE"))
vals$counts <- runScanpyTSNE(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
# useAssay = "scanpyScaledData",
reducedDimName = "scanpyTSNE",
dims = input$scanpy_pca_significant_pc_counter,
perplexity = input$scanpy_perplexity_tsne)
message(paste0(date(), " ... Plotting tSNE"))
output$scanpy_plot_tsne <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyTSNE")
})
})
updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
message(paste0(date(), " ... tSNE Complete"))
}))
#UMAP
observeEvent(input$scanpy_run_umap_button, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
# if(!is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[[input$reduction_umap_method]])){
message(paste0(date(), " ... Running UMAP"))
vals$counts <- runScanpyUMAP(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
# useAssay = "scanpyScaledData", #not needed when using PCA
reducedDimName = "scanpyUMAP",
dims = input$scanpy_pca_significant_pc_counter,
minDist = input$scanpy_min_dist_umap,
nNeighbors = input$scanpy_n_neighbors_umap,
spread = input$scanpy_spread_umap,
alpha = input$scanpy_spread_alpha,
gamma = input$scanpy_spread_gamma)
message(paste0(date(), " ... Plotting UMAP"))
output$scanpy_plot_umap <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyUMAP")
})
})
updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
message(paste0(date(), " ... UMAP Complete"))
}))
#Clustering
observeEvent(input$scanpy_find_clusters_button, withConsoleMsgRedirect(
msg = "Please wait while clusters are being computed. See console log for progress.",
{
req(vals$counts)
#Remove plot tabs if generated before
removeTab(inputId = "scanpyClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "UMAP Plot")
message(paste0(date(), " ... Clustering Dataset"))
vals$counts <- runScanpyFindClusters(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
nNeighbors = input$scanpy_nNeighbors,
dims = input$scanpy_pca_significant_pc_counter,
method = input$scanpy_algorithm.use,
resolution = input$scanpy_resolution_clustering,
niterations = -1,
cor_method = input$scanpy_corr_method)
updateCollapse(session = session, "ScanpyUI", style = list("Clustering" = "success"))
message(paste0(date(), " ... Finding Clusters Complete"))
if("scanpyTSNE" %in% reducedDimNames(vals$counts)){
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotOutput(outputId = "scanpy_plot_tsne_clustering")
)
)
)
message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
output$scanpy_plot_tsne_clustering <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyTSNE",
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
})
})
}
if("scanpyUMAP" %in% reducedDimNames(vals$counts)){
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotOutput(outputId = "scanpy_plot_umap_clustering")
)
), select = TRUE
)
message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
output$scanpy_plot_umap_clustering <- renderPlot({
isolate({
plotScanpyEmbedding(
inSCE = vals$counts,
reducedDimName = "scanpyUMAP",
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering)
)
})
})
}
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotOutput(outputId = "scanpy_plot_pca_clustering")
)
), select = FALSE)
message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
output$scanpy_plot_pca_clustering <- renderPlot({
isolate({
plotScanpyPCA(inSCE = vals$counts,
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
})
})
shinyjs::show(selector = ".scanpy_clustering_plots")
#enable find marker selection
shinyjs::enable(
selector = "#ScanpyUI > div[value='Find Markers']")
#update colData names
updateColDataNames()
#populate updated colData items for findMarkers tab
updateSelectInput(session = session,
inputId = "scanpyFindMarkerSelectPhenotype",
choices = colnames(colData(vals$counts)),
selected = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
}))
observeEvent(input$scanpyFindMarkerRun, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding Marker Genes"))
vals$counts <- runScanpyFindMarkers(inSCE = vals$counts,
colDataName = input$scanpyFindMarkerSelectPhenotype,
test = input$scanpyFindMarkerTest,
corr_method = input$scanpyFindMarkerCorrMethod)
shinyjs::show(selector = ".scanpy_findmarker_table")
shinyjs::show(selector = ".scanpy_findmarker_jointHeatmap")
shinyjs::show(selector = ".scanpy_findmarker_plots")
# Plot heatmap
output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
isolate({
plotScanpyMarkerGenesHeatmap(vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
nGenes = 2)
})
})
message(paste0(date(), " ... Find Markers Complete"))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-scanpy", parent = session,
de = TRUE, fm = TRUE, pa = TRUE)
updateCollapse(session = session, "ScanpyUI", style = list("Find Markers" = "success"))
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Matrix Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerFeaturePlot")
)
)
), select = TRUE
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Matrix Plot",
panel(heading = "Matrix Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerMatrixPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerDotPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "scanpyFindMarkerHeatmapPlot")
)
)
)
)
)
)
vals$ftScanpy <- callModule(
module = filterTableServer,
id = "filterScanpyFindMarker",
dataframe = metadata(vals$counts)$scanpyMarkers,
defaultFilterColumns = c("Pvalue"),
defaultFilterOperators = c("<="),
defaultFilterValues = c("0.05"),
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
}))
observeEvent(input$scanpy_findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
msg = "Please wait while heatmap is being plotted. See console log for progress.",
{
output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
isolate({
plotScanpyMarkerGenesHeatmap(vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
nGenes = input$scanpy_findMarkerHeatmapPlotFullNumeric)
})
})
}))
observe({
req(vals$ftScanpy$data)
req(vals$ftScanpy$selectedRows)
df <- vals$ftScanpy$data[vals$ftScanpy$selectedRows, ]
output$scanpyFindMarkerFeaturePlot <- renderPlot({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyUMAP",
color = df$Gene)
})
output$scanpyFindMarkerViolinPlot <- renderPlot({
plotScanpyViolin(inSCE = vals$counts,
features = df$Gene,
groupBy = input$scanpyFindMarkerSelectPhenotype,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerMatrixPlot <- renderPlot({
plotScanpyMatrixPlot(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerDotPlot <- renderPlot({
plotScanpyDotPlot(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerHeatmapPlot <- renderPlot({
plotScanpyHeatmap(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
})
})
compbiomed/singleCellTK documentation built on Feb. 10, 2024, 3:32 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#1GB max upload size
options(shiny.maxRequestSize = 1000 * 1024 ^ 2)
options(useFancyQuotes = FALSE)
options(shiny.autoreload = TRUE)
seurat.version <- packageVersion(pkg = "SeuratObject")
internetConnection <- suppressWarnings(Biobase::testBioCConnection())
source("partials.R", local = TRUE) # creates several smaller UI components
# R.utils::sourceDirectory("qc_help_pages")
source("qc_help_pages/ui_decontX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_soupX_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_bcds_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_cxds_bcds_hybrid_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_doubletFinder_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scrublet_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_dc_and_qcm_help.R", local = TRUE) # creates several smaller UI components
source("qc_help_pages/ui_scDblFinder_help.R", local = TRUE) # creates several smaller UI components
# source("server_partials/server_01_data.R", local = TRUE) # functions for Data section
# Define server logic required to draw a histogram
shinyServer(function(input, output, session) {
# PushBar setup
# setup_pushbar(blur = FALSE, overlay = FALSE)
# library(fs)
# library(shinyFiles)
#-----------------------------------------------------------------------------
# MISC - Used throughout app
#-----------------------------------------------------------------------------
#reactive values object
vals <- reactiveValues(
counts = getShinyOption("inputSCEset"),
original = getShinyOption("inputSCEset"),
batchRes = NULL,
gsvaRes = NULL,
gsvaLimma = NULL,
vamRes = NULL,
vamCdf = NULL,
vamResults = NULL,
vamScore = NULL,
gsvaScore = NULL,
gsvaResults = NULL,
visplotobject = NULL,
enrichRes = NULL,
celdaMod = NULL,
celdaList = NULL,
celdaListAll = NULL,
celdaListAllNames = NULL,
celdatSNE = NULL,
celdaModuleFeature = NULL,
dimRedPlot = NULL,
dimRedPlot_geneExp = NULL,
dendrogram = NULL,
pcX = NULL,
pcY = NULL,
showAssayDetails = FALSE,
hmCSPresets = list("RWB" = c("blue", "white", "red"),
"RdBu_r" = c("#2971b1", "#f7f6f6", "#b92732"),
"BrBG" = c("#995d12", "#f4f4f4", "#0c7068"),
"Blues" = c("#dae8f5", "#6daed4", "#0b559f"),
"Greens" = c("#E1F3F6", "#69C2A1", "#04702F")),
hmCSURL = NULL,
hmTmpColData = NULL,
hmTmpRowData = NULL,
hvgCalculated = list(status = FALSE, method = NULL),
fmHMshowHide = FALSE
)
#Update all of the columns that depend on pvals columns
updateColDataNames <- function(){
pdataOptions <- colnames(colData(vals$counts))
updateSelectInput(session, "soupXCluster", choices = c("None", pdataOptions))
updateSelectInput(session, "qcSampleSelect", choices = pdataOptions)
updateSelectInput(session, "filteredSample",
choices = c("none", pdataOptions))
updateSelectInput(session, "deleterowdatacolumn",
choices = pdataOptions)
updateSelectInput(session, "colorBy",
choices = c("No Color", "Gene Expression", pdataOptions))
updateSelectInput(session, "shapeBy",
choices = c("No Shape", pdataOptions))
updateSelectInput(session, "scMergeCT",
choices = c(pdataOptions))
updateSelectInput(session, "combatCond",
choices = pdataOptions)
updateSelectInput(session, "combatBioCond",
choices = c("None", pdataOptions))
updateSelectInput(session, "batchCorrVar",
choices = pdataOptions)
updateSelectInput(session, "batchCheckVar",
choices = pdataOptions)
updateSelectInput(session, "batchCheckCond",
choices = c("None", pdataOptions))
updateSelectInput(session, "clustVisCol", choices = pdataOptions)
updateSelectInput(session, "deC1Class",
choices = pdataOptions)
updateSelectInput(session, "deC2G1Col",
choices = pdataOptions)
updateSelectInput(session, "deC2G2Col",
choices = pdataOptions)
updateSelectInput(session, 'deCovar', choices = pdataOptions)
updateSelectInput(session, "deHMcolData",
choices = pdataOptions)
updateSelectInput(session, "deHMSplitCol",
choices = c('condition', pdataOptions),
selected = 'condition')
updateSelectInput(session, "fmCluster", choices = pdataOptions)
updateSelectInput(session, "fmHMcolData",
choices = pdataOptions)
updateSelectInput(session, "hmCellAnn", choices = pdataOptions)
updateSelectInput(session, "pathwayPlotVar",
choices = pdataOptions)
updateSelectInput(session, "selectReadDepthCondition",
choices = pdataOptions)
updateSelectInput(session, "selectCellNumCondition",
choices = pdataOptions)
updateSelectInput(session, "selectSnapshotCondition",
choices = pdataOptions)
updateSelectInput(session, "annotModifyChoice",
choices = c("none", pdataOptions))
updateSelectInput(session, "hmCellCol",
choices = pdataOptions)
updateSelectInput(session, "hmCellTextBy",
choices = c("Row Names", pdataOptions))
updateSelectInput(session, 'hmAddCellLabel',
choices = c("Default cell IDs", pdataOptions))
updateSelectInput(session, "ctLabelByCluster",
choices = pdataOptions)
if (!is.null(hmTemp$sce)) {
hmAnnAllColors$col <- dataAnnotationColor(hmTemp$sce, 'col')
}
updateSelectInput(session, "TSCANclusterName",
choices = c("Auto generate clusters", pdataOptions))
}
updateGeneNames <- function(){
selectthegenes <- rownames(vals$counts)
updateSelectizeInput(session, "colorGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "selectvisGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "enrichGenes",
choices = selectthegenes, server = TRUE)
updateSelectizeInput(session, "plotTSCANDimReduceFeatures_features",
choices = selectthegenes, server = TRUE)
}
updateFeatureAnnots <- function(){
selectRowData <- colnames(rowData(vals$counts))
my_list <- data.frame()
for(i in selectRowData) {
my_list[i,1] <- paste0(i, " (e.g. ", paste(head(rowData(vals$counts)[,i], n = 3), collapse = ","), ")")
}
selectRowDataWithExamples <- as.character(my_list[,1])
selectNonNARowData <- names(apply(rowData(vals$counts), 2, anyNA)[apply(rowData(vals$counts), 2, anyNA) == FALSE])
my_list2 <- data.frame()
for(j in selectNonNARowData) {
my_list2[j,1] <- paste0(j, " (e.g. ", paste(head(rowData(vals$counts)[,j], n = 3), collapse = ","), ")")
}
selectNonNARowDataWithExamples <- as.character(my_list2[,1])
Default <- paste0("Default (e.g. ", paste(head(rownames(vals$counts), n = 3), collapse = ","), ")")
updateSelectInput(session, "gsByParam",
choices = c("rownames", selectRowData))
updateSelectInput(session, "importFeatureDispOpt",
choices = c(Default,
selectRowDataWithExamples))
updateSelectInput(session, "importFeatureNamesOpt",
choices = c(Default,
selectNonNARowDataWithExamples))
updateSelectInput(session, "filteredFeature",
choices = c("none", selectRowData))
updateSelectInput(session, "hvgPlotFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "fmHMFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "deHMrowData",
choices = selectRowData)
updateSelectInput(session, "deHMSplitRow",
choices = c('regulation', selectRowData),
selected = 'regulation')
updateSelectInput(session, "deHMrowLabel",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "deVolcFeatureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'deVioLabel',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'deRegLabel',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'tscanDEFeatureDisplay',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, 'plotTSCANClusterDEG_featureDisplay',
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
choices = c("Rownames (Default)",
selectRowData))
updateSelectInput(session, "fmHMrowData",
choices = selectRowData)
updateSelectInput(session, "hmGeneCol",
choices = selectRowData)
updateSelectInput(session, "hmGeneTextBy",
choices = c("Row Names", selectRowData))
updateSelectInput(session, 'hmGeneAnn', choices = selectRowData)
updateSelectInput(session, 'hmAddGeneLabel',
choices = c("Default feature IDs", selectRowData))
if (!is.null(hmTemp$sce)) {
hmAnnAllColors$row <- dataAnnotationColor(hmTemp$sce, 'row')
}
}
updateNumSamples <- function(){
numsamples <- ncol(vals$counts)
updateNumericInput(session, "downsampleNum", value = numsamples,
max = numsamples)
}
updateSelectInputTag <- function(session, inputId, choices = NULL,
selected = NULL, label = "Select assay:",
tags = NULL, recommended = NULL,
showTags = TRUE, redDims = FALSE,
inSCE = vals$counts){
choices <- expTaggedData(inSCE, tags, redDims = redDims, showTags = showTags, recommended = recommended)
updateSelectizeInput(session = session, inputId = inputId, label = label, choices = choices, selected = selected)
}
updateAssayInputs <- function(){
currassays <- names(assays(vals$counts))
updateSelectInputTag(session, "dimRedAssaySelect",
label = "Select Input Matrix:",
recommended = c("transformed", "hvg"),
choices = expDataNames(vals$counts))
updateSelectInputTag(session, "dimRedAssaySelect_tsneUmap",
label = "Select Input Matrix:",
recommended = c("redDims"),
redDims = TRUE)
updateSelectInputTag(session, "batchCheckAssay", choices = currassays)
updateSelectInputTag(session, "batchCheckOrigAssay", choices = currassays)
updateSelectInputTag(session, "clustScranSNNMat",
label = "Select Input Matrix:",
choices = expDataNames(vals$counts),
recommended = "redDims", redDims = TRUE)
if (is.null(input$deMethod)) {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed"), redDims = TRUE)
} else if (input$deMethod == "DESeq2") {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "uncategorized"),
recommended = c("raw"))
} else {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed"), redDims = TRUE)
}
if (is.null(input$fmMethod)) {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
} else if (input$fmMethod == "DESeq2") {
updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
} else {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed"))
}
updateSelectInputTag(session, "fmHMAssay", choices = currassays,
selected = input$fmAssay)
updateSelectInputTag(session, "pathwayAssay",
recommended = c("transformed", "normalized", "scaled"))
updateSelectInputTag(session, "vamAssay",
recommended = c("transformed", "normalized", "scaled"))
updateSelectInputTag(session, "modifyAssaySelect")
updateSelectInputTag(session, "normalizeAssaySelect",
label = "Select assay to normalize:",
recommended = "raw")
updateSelectInputTag(session, "seuratSelectNormalizationAssay",
choices = currassays, showTags = FALSE)
updateSelectInputTag(session, "scanpySelectNormalizationAssay",
choices = currassays, showTags = FALSE)
if(input$hvgMethodFS == "vst"){
updateSelectInputTag(session, "assaySelectFS_Norm", recommended = c("raw"))
}
else{
updateSelectInputTag(session, "assaySelectFS_Norm",
recommended = c("transformed", "normalized"))
}
updateSelectInputTag(session, "filterAssaySelect", choices = currassays)
updateSelectInputTag(session, "qcAssaySelect", recommended = "raw", inSCE = vals$original)
updateSelectInputTag(session, "celdaAssay", choices = currassays)
updateSelectInputTag(session, "celdaAssayGS", choices = currassays)
updateSelectInputTag(session, "celdaAssaytSNE", choices = currassays)
updateSelectInputTag(session, "celdaAssayProbabilityMap",
choices = currassays)
updateSelectInputTag(session, "celdaAssayModuleHeatmap",
choices = currassays)
updateSelectInputTag(session, "depthAssay", choices = currassays)
updateSelectInputTag(session, "cellsAssay", choices = currassays)
updateSelectInputTag(session, "snapshotAssay", choices = currassays)
updateSelectInputTag(session, "exportAssay", choices = currassays)
updateSelectInputTag(session, "hmAssay", recommended = "transformed")
updateSelectInputTag(session, "bpAssay", recommended = "transformed")
updateSelectInputTag(session, "ctLabelAssay", choices = currassays,
recommended = c("transformed"))
# batch correction assay conditions
bc.recommended <- NULL
method.log <- c("FastMNN", "Limma", "MNN")
method.scale <- c("BBKNN")
method.raw <- c("ZINBWaVE", "ComBatSeq")
if (is.null(input$batchCorrMethods)) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.log) {
bc.recommended <- c("transformed")
} else if (input$batchCorrMethods %in% method.raw) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.scale) {
bc.recommended <- "scaled"
}
updateSelectInputTag(session, "batchCorrAssay",
label = "Select Assay to Correct:",
choices = currassays,
recommended = bc.recommended)
updateSelectInputTag(session, "AdvancedMethodSelect_Colorby",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "AdvancedMethodSelect_Xaxis",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "AdvancedMethodSelect_Yaxis",
label = h5("Advanced Method"),
choices = currassays)
updateSelectInputTag(session, "TSCANassayselect", choices = currassays,
recommended = "transformed")
updateSelectInputTag(session, "TSCANBranchAssaySelect",
choices = currassays,
recommended = "transformed")
updateSelectInputTag(session, "plotTSCANDimReduceFeatures_useAssay",
choices = currassays,
recommended = "transformed")
}
updateGeneSetSelection <- function() {
allGS <- sctkListGeneSetCollections(vals$counts)
if (length(allGS) == 0) {
updateSelectizeInput(session, "PathwayGeneLists",
choices = "Import geneset before using")
} else {
updateSelectizeInput(session, "PathwayGeneLists", choices = allGS)
}
updateSelectInput(session, "gsExisting", choices = c("None", allGS))
names(allGS) <- allGS
updateSelectInput(session, "QCMgeneSets", choices =c("None", allGS))
}
observeEvent(vals$original, {
if (!is.null(vals$original)) {
#if (!is.null(metadata(vals$original)$sctk$genesets)) {
#newGSchoices <- sctkListGeneSetCollections(vals$original)
#updateSelectInput(session, "gsExisting", choices = c("None", newGSchoices))
#updateSelectInput(session, "QCMgeneSets", choices =c("None", newGSchoices))
#shinyjs::show(id = "gsAddToExisting", anim = FALSE)
#} else {
#shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
#updateSelectInput(session, "gsExisting", choices = c("None"), selected = "None")
#updateSelectInput(session, "QCMgeneSets", choices =c("None"), selected = "None")
#}
shinyjs::show(id="combineOptions")
#gsByChoices <- c("None", "rownames", names(rowData(vals$original)))
#updateSelectInput(session, "gsByParam", choices = gsByChoices, selected = "rownames")
} else {
shinyjs::hide(id="combineOptions")
}
})
updateReddimInputs <- function(){
currreddim <- names(reducedDims(vals$counts))
updateSelectInput(session, "FastMNNReddim", choices = currreddim)
updateSelectInput(session, "HarmonyReddim", choices = currreddim)
updateSelectInput(session, "selectRedDimPlot_tsneUmap",
choices = currreddim)
updateSelectInput(session, "clustVisReddim", choices = currreddim)
updateSelectInput(session, "clustKMeansReddim", choices = currreddim)
updateSelectInput(session, "clustSeuratReddim", choices = currreddim)
updateSelectInput(session, "QuickAccess",
choices = c(currreddim, "Custom"))
updateSelectInput(session, "ApproachSelect_Xaxis", choices = currreddim)
updateSelectInput(session, "ApproachSelect_Yaxis", choices = currreddim)
updateSelectInput(session, "ApproachSelect_Colorby", choices = currreddim)
suppressWarnings({availPathwayRes <- getPathwayResultNames(vals$counts)})
updateSelectizeInput(session, "pathwayRedDimNames",
choices = availPathwayRes)
updateSelectInput(session, "TSCANReddim", choices = currreddim)
updateSelectInput(session, "TSCANVisRedDim", choices = currreddim)
updateSelectInput(session, "DEClusterRedDimNames", choices = currreddim)
updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim", choices = currreddim)
updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim", choices = currreddim)
}
observeEvent(input$consoleToggle, {
shinyjs::toggle(id = "consolePanel")
})
# Stop auto-scroll console tab
observeEvent(input$logDataAutoScrollStatus, {
stopAutoScroll <- paste0("clearInterval(", input$logDataAutoScrollStatus, ");")
shinyjs::runjs(stopAutoScroll)
})
# js$disableTabs()
# Close app on quit
# session$onSessionEnded(stopApp)
#-----------------------------------------------------------------------------
# Page 1: Upload ####
#-----------------------------------------------------------------------------
sysname <- Sys.info()[['sysname']]
if (sysname == "Windows") {
roots <- getVolumes()()
} else {
roots <- c(home = "~/")
}
dirPaths <- reactiveValues(
bDirectory = ".",
sDirectory = ".",
directory = ".",
outputDirectory = "."
)
# Upload data through shiny app
allImportEntries <- reactiveValues(samples=list(), id_count=0)
shinyDirChoose(input, "bDirectory", roots = roots)
shinyDirChoose(input, "sDirectory", roots = roots)
shinyDirChoose(input, 'directory', roots = roots)
output$bDirectoryPath <- renderText({
dirPaths$bDirectory
})
output$sDirectoryPath <- renderText({
dirPaths$sDirectory
})
output$directoryPath <- renderText({
dirPaths$directory
})
# event listener for the base directory modal (need to populate table for sample names)
# see https://github.com/wleepang/shiny-directory-input
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$bDirectory
},
handlerExpr = {
if ("path" %in% names(input$bDirectory)) {
# condition prevents handler execution on initial app launch
#path = choose.dir(default = readDirectoryInput(session, 'bDirectory'),
# caption="Choose a directory")
#updateDirectoryInput(session, 'bDirectory', value = path)
vol <- roots[[input$bDirectory$root]]
dirPaths$bDirectory <- paste0(vol, paste(unlist(input$bDirectory$path[-1]),
collapse = .Platform$file.sep))
path <- dirPaths$bDirectory
# clear the previous table of sample names
prevPath <- path
count <- 0
for (prev in list.dirs(prevPath, recursive = FALSE)) {
count <- count+1
removeUI(
selector = paste0("#sampleRow", count)
)
}
# create a new table for the selected directory
count <- 0
if (!is.na(path)) {
# Add Reference selection for cellRangerV2
if (input$uploadChoice == "cellRanger2") {
## Identify available reference
firstSampleDir <- list.dirs(path, recursive = FALSE)[1]
refPath <- file.path(firstSampleDir, "outs/filtered_gene_bc_matrices")
refList <- basename(list.dirs(refPath, recursive = FALSE))
## Add UI
insertUI(
selector = "#bDirTable",
ui = fluidRow(
column(
6,
selectInput("cr2_b_Ref", "Reference:", refList)
)
)
)
}
# Add Sample Rename rows
counts <- vector()
for (sample in list.dirs(path, recursive = FALSE)) {
count <- count+1
counts <- c(counts, count)
insertUI(
selector = "#bDirTable",
ui = fluidRow(
id = paste0("sampleRow", count),
column(6, basename(sample)),
column(6, textAreaInput(paste0("sampleName", count), "Sample Name", resize = "none", value = basename(sample)))
)
)
}
}
}
}
)
# for sample directory modal
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$sDirectory
},
handlerExpr = {
#if (input$sDirectory > 0) {
# # condition prevents handler execution on initial app launch
# path = choose.dir(default = readDirectoryInput(session, 'sDirectory'),
# caption="Choose a directory")
# updateDirectoryInput(session, 'sDirectory', value = path)
# if (!is.na(path)) {
# updateTextInput(session, "sSampleID", value = basename(path))
# }
#}
if ("path" %in% names(input$sDirectory)) {
vol <- roots[[input$sDirectory$root]]
dirPaths$sDirectory <- paste0(vol, paste(unlist(input$sDirectory$path[-1]),
collapse = .Platform$file.sep))
path <- dirPaths$sDirectory
if (!is.na(path)) {
if (input$uploadChoice == "cellRanger2") {
## Identify available reference
refPath <- file.path(path, "outs/filtered_gene_bc_matrices")
refList <- basename(list.dirs(refPath, recursive = FALSE))
## Add UI
insertUI(
selector = "#sDirTable",
ui = fluidRow(
column(
6,
selectInput("cr2_s_Ref", "Reference:", refList)
)
)
)
}
updateTextInput(session, "sSampleID", value = basename(path))
}
}
}
)
observeEvent(
ignoreNULL = TRUE,
eventExpr = {
input$directory
},
handlerExpr = {
#if (input$directory > 0) {
# # condition prevents handler execution on initial app launch
# path = choose.dir(default = readDirectoryInput(session, 'directory'),
# caption="Choose a directory")
# updateDirectoryInput(session, 'directory', value = path)
#}
if ("path" %in% names(input$directory)) {
vol <- roots[[input$directory$root]]
dirPaths$directory <- paste0(vol, paste(unlist(input$directory$path[-1]),
collapse = .Platform$file.sep))
}
}
)
# event listeners for "Add Sample" buttons
observeEvent(input$addCR2Sample, {
showModal(importCRModal())
})
observeEvent(input$crOpt1, {
removeModal()
showModal(importCRBDir())
})
observeEvent(input$crOpt2, {
removeModal()
showModal(importCRSDir())
})
observeEvent(input$crOpt3, {
removeModal()
showModal(importCRDDir())
})
observeEvent(input$addCR3Sample, {
showModal(importCRModal())
})
observeEvent(input$addSSSample, {
showModal(importStarModal())
})
observeEvent(input$addBUSSample, {
showModal(importModal())
})
observeEvent(input$addSEQSample, {
showModal(importModal(needsDir = TRUE))
})
observeEvent(input$addOptSample, {
showModal(importModal())
})
# event listener for "Remove Sample" buttons
observeEvent(input$clearAllImport, {
for (entry in allImportEntries$samples) {
removeUI(selector = paste0("#", entry$id))
}
allImportEntries$samples <- list()
})
# base directory
observeEvent(input$BDirOK, {
basePath <- dirPaths$bDirectory
# if the user doesn't specify a base directory, show the modal again with the warning message
if (identical(basePath, character(0))) {
showModal(importCRBDir(failed = TRUE))
} else {
allDirs <- list.dirs(basePath, recursive = FALSE)
# if we are adding a new CellRangerV2 sample
if (input$uploadChoice == "cellRanger2") {
allUI <- vector()
allIDs <- vector()
count <- 0
for (sample in allDirs) {
count <- count + 1
name <- input[[paste0("sampleName", count)]]
if (!nzchar(name)) {
name <- basename(sample)
}
id <- paste0("bnewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(cellRangerDirs = basePath,
sampleDirs = basename(sample),
sampleNames = name,
reference = input$cr2_b_Ref))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
ui_i <- fluidRow(
id = id,
tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
column(3, basePath),
column(3, basename(sample)),
column(3, name),
column(3, actionButton(paste0("remove", id), "X"))
)
allImportEntries$id_count <- allImportEntries$id_count + 1
allUI <- c(allUI, list(ui_i))
allIDs <- c(allIDs, id)
}
} else { # if we are adding a new CellRangerV3 sample
allUI <- vector()
allIDs <- vector()
count <- 0
for (sample in allDirs) {
count <- count + 1
name <- input[[paste0("sampleName", count)]]
if (!nzchar(name)) {
name <- basename(sample)
}
id <- paste0("bnewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = basePath, sampleDirs = basename(sample), sampleNames = name))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
removeBtnStyle <- paste0(paste0("#remove", id), "{padding-top: 0; padding-bottom: 0;}")
ui_i <- fluidRow(
id = id,
tags$style(HTML(paste0(fluidRowStyle, removeBtnStyle))),
column(3, basePath),
column(3, basename(sample)),
column(3, name),
column(3, actionButton(paste0("remove", id), "X"))
)
allImportEntries$id_count <- allImportEntries$id_count + 1
allUI <- c(allUI, list(ui_i))
allIDs <- c(allIDs, id)
}
}
# insert all the new sample rows
for (i in seq_along(allUI)) {
insertUI(
selector = "#newSampleImport",
ui = allUI[i]
)
}
# create event handlers for all the remove buttons
# from: https://stackoverflow.com/questions/40038749/r-shiny-how-to-write-loop-for-observeevent
lapply(
X = allIDs,
FUN = function(id_i){
observeEvent(input[[paste0("remove", id_i)]], {
removeUI(
selector = paste0("#", id_i)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id_i) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
}
)
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event listeners for Cell Ranger import modals' OK buttons
# sample directory
observeEvent(input$SDirOK, {
samplePath <- dirPaths$sDirectory
# make sure a directory is selected
if (identical(samplePath, character(0))) {
showModal(importCRSDir(failed = TRUE))
} else {
# add the files to the appropriate reactiveValues
if (input$uploadChoice == "cellRanger2") {
id <- paste0("snewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(cellRangerDirs = dirname(samplePath),
sampleDirs = basename(samplePath),
sampleNames = input$sSampleID,
reference = input$cr2_s_Ref))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
} else {
id <- paste0("snewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(cellRangerDirs = paste0(dirname(samplePath), "/"), sampleDirs = basename(samplePath), sampleNames = input$sSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
}
# add new row to table
addToGeneralSampleTable(input$uploadChoice, id, samplePath, input$sSampleID)
# handler to remove the sample that was just added
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# data directory
observeEvent(input$DDirOK, {
dataPath <- dirPaths$directory
if ((!nzchar(input$dSampleID)) || (identical(dataPath, character(0)))) {
showModal(importCRDDir(failed = TRUE))
} else {
if (input$uploadChoice == "cellRanger2") {
id <- paste0("dnewSampleCR2", allImportEntries$id_count)
entry <- list(type="cellRanger2", id=id,
params=list(dataDir = dataPath,
sampleName = input$dSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
} else {
id <- paste0("dnewSampleCR3", allImportEntries$id_count)
entry <- list(type="cellRanger3", id=id, params=list(dataDir = dataPath, sampleName = input$dSampleID))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count + 1
}
# add new row to table
addToGeneralSampleTable(input$uploadChoice, id, dataPath, input$dSampleID)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event handler for pressing OK on the import Star modal
observeEvent(input$modalStarOk, {
basePath <- dirPaths$directory
curFiles <- list()
if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
showModal(importStarModal(failed = TRUE))
} else {
entry <- list()
id <- paste0("newSampleSS", allImportEntries$id_count)
entry <- list(type="starSolo", id = id, params=list(STARsoloDirs = basePath, samples = input$sampleName, STARsoloOuts = input$geneFolder))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# event handler for pressing OK on the import modal
observeEvent(input$modalOk, {
basePath <- dirPaths$directory
curFiles <- list()
if ((!nzchar(input$sampleName)) || (identical(basePath, character(0)))) {
showModal(importModal(failed = TRUE))
} else {
entry <- list()
if (input$uploadChoice == "busTools") {
id <- paste0("newSampleBUS", allImportEntries$id_count)
entry <- list(type="busTools", id = id, params=list(BUStoolsDirs = basePath, samples = input$sampleName))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
} else if (input$uploadChoice == "seqc") {
id <- paste0("newSampleSEQ", allImportEntries$id_count)
entry <- list(type="seqc", id = id, params=list(seqcDirs = basePath, prefix = input$sampleID, samples = input$sampleName))
updateTextInput(session, "sampleID", value = "")
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
} else if (input$uploadChoice == "optimus") {
id <- paste0("newSampleOpt", allImportEntries$id_count)
entry <- list(type="optimus", id = id, params=list(OptimusDirs = basePath, samples = input$sampleName))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
}
addToGeneralSampleTable(input$uploadChoice, id, basePath, input$sampleName)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
removeModal()
}
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from CR3
observeEvent(input$addFilesImport_custom, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="cellRanger3_files", id = id, params=list(assayFile = input$countsfile_custom$datapath, annotFile = input$annotFile_custom$datapath,
featureFile = input$featureFile_custom$datapath,
summaryFile = input$summaryFile_custom$datapath,
sampleName = input$sampleNameCR,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom$datapath)
}
if (!is.null(input$annotFile_custom$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom$datapath)
}
if (!is.null(input$featureFile_custom$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom$datapath)
}
if (!is.null(input$summaryFile_custom$datapath)) {
summaryFileCol <- paste0("Metrics: ", input$summaryFile_custom$datapath)
}
if (!is.null(input$sampleNameCR)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameCR)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameCR)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from starSolo
observeEvent(input$addFilesImport_custom_starSolo, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="starSolo_files", id = id, params=list(assayFile = input$countsfile_custom_starSolo$datapath, annotFile = input$annotFile_custom_starSolo$datapath,
featureFile = input$featureFile_custom_starSolo$datapath,
sampleName = input$sampleNameSS,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom_starSolo$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom_starSolo$datapath)
}
if (!is.null(input$annotFile_custom_starSolo$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom_starSolo$datapath)
}
if (!is.null(input$featureFile_custom_starSolo$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom_starSolo$datapath)
}
if (!is.null(input$sampleNameSS)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameSS)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameSS)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from busTools
observeEvent(input$addFilesImport_custom_busTools, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="busTools_files", id = id, params=list(assayFile = input$countsfile_custom_busTools$datapath, annotFile = input$annotFile_custom_busTools$datapath,
featureFile = input$featureFile_custom_busTools$datapath,
sampleName = input$sampleNameBT,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
summaryFileCol <- ""
sampleFileName <- ""
if (!is.null(input$countsfile_custom_busTools$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile_custom_busTools$datapath)
}
if (!is.null(input$annotFile_custom_busTools$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile_custom_busTools$datapath)
}
if (!is.null(input$featureFile_custom_busTools$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile_custom_busTools$datapath)
}
if (!is.null(input$sampleNameSS)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameBT)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol, summaryFileCol), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameBT)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from optimus
observeEvent(input$addFilesImport_custom_optimus, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="optimus_files", id = id, params=list(matrixLocation = input$matrix_custom_optimus$datapath,
colIndexLocation = input$colIndex_custom_optimus$datapath,
rowIndexLocation = input$rowIndex_custom_optimus$datapath,
cellMetricsLocation = input$cellMetrics_custom_optimus$datapath,
geneMetricsLocation = input$geneMetrics_custom_optimus$datapath,
emptyDropsLocation = input$emptyDrops_custom_optimus$datapath,
sampleName = input$sampleNameOP,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
matrixFileCol <- ""
colIndexFileCol <- ""
rowIndexFileCol <- ""
cellMetricsFileCol <- ""
geneMetricsFileCol <- ""
emptyDropsFileCol <- ""
sampleFileName <- ""
if (!is.null(input$matrix_custom_optimus$datapath)) {
matrixFileCol <- paste0("Matrix: ", input$matrix_custom_optimus$datapath)
}
if (!is.null(input$colIndex_custom_optimus$datapath)) {
colIndexFileCol <- paste0("colIndex: ", input$colIndex_custom_optimus$datapath)
}
if (!is.null(input$rowIndex_custom_optimus$datapath)) {
rowIndexFileCol <- paste0("rowIndex: ", input$rowIndex_custom_optimus$datapath)
}
if (!is.null(input$cellMetrics_custom_optimus$datapath)) {
cellMetricsFileCol <- paste0("cellMetrics: ", input$cellMetrics_custom_optimus$datapath)
}
if (!is.null(input$geneMetrics_custom_optimus$datapath)) {
geneMetricsFileCol <- paste0("geneMetrics: ", input$geneMetrics_custom_optimus$datapath)
}
if (!is.null(input$emptyDrops_custom_optimus$datapath)) {
emptyDropsFileCol <- paste0("emptyDrops: ", input$emptyDrops_custom_optimus$datapath)
}
if (!is.null(input$sampleNameOP)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameOP)
}
locCol <- paste(c(matrixFileCol, colIndexFileCol, rowIndexFileCol, cellMetricsFileCol, geneMetricsFileCol, emptyDropsFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameOP)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input from seqc
observeEvent(input$addFilesImport_custom_seqc, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="seqc_files", id = id, params=list(readCountsLocation = input$readCounts_custom_seqc$datapath,
moleculeCountsLocation = input$moleculeCounts_custom_seqc$datapath,
barcodesLocation = input$barcodes_custom_seqc$datapath,
genesLocation = input$genes_custom_seqc$datapath,
sampleName = input$sampleNameSC,
assayName = "counts"))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
matrixFileCol <- ""
molFileCol <- ""
barcodesFileCol <- ""
genesFileCol <- ""
sampleFileName <- ""
if (!is.null(input$readCounts_custom_seqc$datapath)) {
matrixFileCol <- paste0("Matrix: ", input$readCounts_custom_seqc$datapath)
}
if (!is.null(input$moleculeCounts_custom_seqc$datapath)) {
molFileCol <- paste0("molCounts: ", input$moleculeCounts_custom_seqc$datapath)
}
if (!is.null(input$barcodes_custom_seqc$datapath)) {
barcodesFileCol <- paste0("barcodes: ", input$barcodes_custom_seqc$datapath)
}
if (!is.null(input$genes_custom_seqc$datapath)) {
genesFileCol <- paste0("genes: ", input$genes_custom_seqc$datapath)
}
if (!is.null(input$sampleNameSC)) {
sampleFileName <- paste0("Sample Name: ", input$sampleNameSC)
}
locCol <- paste(c(matrixFileCol, molFileCol, barcodesFileCol, genesFileCol, sampleFileName), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$sampleNameSC)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import a file input
observeEvent(input$addFilesImport, {
id <- paste0("newSampleFiles", allImportEntries$id_count)
entry <- list(type="files", id = id, params=list(assayFile = input$countsfile$datapath, annotFile = input$annotFile$datapath,
featureFile = input$featureFile$datapath, assayName = input$inputAssayType))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
assayFileCol <- ""
annotFileCol <- ""
featureFileCol <- ""
if (!is.null(input$countsfile$datapath)) {
assayFileCol <- paste0("Assay: ", input$countsfile$datapath)
}
if (!is.null(input$annotFile$datapath)) {
annotFileCol <- paste0("Annotation: ", input$annotFile$datapath)
}
if (!is.null(input$featureFile$datapath)) {
featureFileCol <- paste0("Features: ", input$featureFile$datapath)
}
locCol <- paste(c(assayFileCol, annotFileCol, featureFileCol), collapse = "\n")
addToGeneralSampleTable("files", id, locCol, input$inputAssayType)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import an example input
observeEvent(input$addExampleImport, {
id <- paste0("newSampleExample", allImportEntries$id_count)
entry <- list(type="example", id = id, params=list(dataset = input$selectExampleData))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
scRNAseqDatasets <- c("fluidigm_pollen", "allen_tasic", "NestorowaHSCData")
tenxPbmcDatasets <- c("pbmc3k", "pbmc4k", "pbmc6k", "pbmc8k", "pbmc33k", "pbmc68k")
locCol <- ""
if (input$selectExampleData %in% scRNAseqDatasets) {
locCol <- "scRNA"
} else {
locCol <- "TENx"
}
addToGeneralSampleTable("example", id, locCol, input$selectExampleData)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
# Event handler to import an RDS input
observeEvent(input$addRDSImport, {
id <- paste0("newSampleRDS", allImportEntries$id_count)
entry <- list(type="rds", id = id, params=list(rdsFile=input$rdsFile$datapath))
allImportEntries$samples <- c(allImportEntries$samples, list(entry))
allImportEntries$id_count <- allImportEntries$id_count+1
addToGeneralSampleTable("rds", id, input$rdsFile$datapath, "")
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in allImportEntries$samples) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
allImportEntries$samples <- allImportEntries$samples[toRemove]
})
updateCollapse(session = session, "importUI", open = "2. Create dataset:",
style = list("1. Add sample to import:" = "success"))
})
observeEvent(input$backToStepOne, {
updateCollapse(session = session, "importUI",
open = "1. Add sample to import:")
updateRadioButtons(session = session, "uploadChoice", selected = "cellRanger3")
})
# Event handler for "Upload" button on import page
observeEvent(input$uploadData, withConsoleMsgRedirect(
msg = "Please wait while data is being imported. See console log for progress.",
{
if (length(allImportEntries$samples) == 0) {
stop("You have not selected any samples to import.")
}
sceObj <- importMultipleSources(allImportEntries)
if (input$combineSCEChoice == "addToExistingSCE") {
if(!is.null(vals$original)) {
sceList <- list(vals$original, sceObj)
vals$original <- combineSCE(sceList = sceList,
by.r = NULL,
by.c = Reduce(intersect, lapply(sceList, function(x) { colnames(colData(x))})),
combined = TRUE)
} else {
vals$original <- sceObj
}
} else {
vals$original <- sceObj
}
# clear table and empty reactive
for (entry in allImportEntries$samples) {
removeUI(selector = paste0("#", entry$id))
}
allImportEntries$samples <- list()
# Add sample variable if it was not included
if (!"sample" %in% names(colData(vals$original)) &&
!"Sample" %in% names(colData(vals$original))) {
sampleVar <- "sample"
# Let the sample name of all cells be "sample"
colData(vals$original)$sample = sampleVar
} else if ("sample" %in% names(colData(vals$original))) {
sampleVar <- "sample"
} else {
sampleVar <- "Sample"
}
if (!is.null(vals$original)) {
vals$counts <- vals$original
#store assayType information in the metadata
# if (!"assayType" %in% names(metadata(vals$counts))) {
# vals$counts <- expSetDataTag(
# inSCE = vals$counts,
# assayType = "raw",
# assays = assayNames(vals$counts))
# }
if (any(duplicated(rownames(vals$counts)))) {
warning("Duplicated rownames detected, making them unique...")
vals$counts <- dedupRowNames(vals$counts)
}
# add feature ids (from rownames) to rowData if rowData is empty
if (ncol(rowData(vals$counts)) < 1){
if(!is.null(rownames(vals$counts))){
rowData(vals$counts) <- S4Vectors::DataFrame(feature_id = rownames(vals$counts))
}
}
# ToDo: Remove these automatic updates and replace with
# observeEvents functions that activate upon the tab selection
updateColDataNames()
updateSelectInput(session, "qcSampleSelect", selected = sampleVar)
updateFeatureAnnots()
updateNumSamples()
updateAssayInputs()
updateGeneNames()
updateReddimInputs()
shinyjs::show(id="annotationData")
js$enableTabs()
updateGeneSetSelection()
} else {
shinyalert::shinyalert("Error!", "The data upload failed!",
type = "error")
}
vals$gsvaRes <- NULL
vals$vamRes <- NULL
vals$vamResults <- NULL
vals$gsvaResults <- NULL
vals$gsvaLimma <- NULL
vals$vamScore <- NULL
vals$gsvaScore <- NULL
vals$visplotobject <- NULL
vals$enrichRes <- NULL
vals$dimRedPlot <- NULL
vals$dimRedPlot_geneExp <- NULL
vals$dendrogram <- NULL
vals$pcX <- NULL
vals$pcY <- NULL
vals$batchRes <- NULL
vals$hvgCalculated <- list(status = FALSE, method = NULL)
dbList <- getMSigDBTable()
geneSetDBChoices <- formatGeneSetDBChoices(dbIDs = dbList$ID, dbCats = dbList$Category_Description)
updateCheckboxGroupInput(session, 'geneSetDB', choices = geneSetDBChoices)
updateSeuratUIFromRDS(vals$counts)
cleanGSTable()
updateHVGMetricSelection()
updateHVGListSelection()
updateDEAnalysisNames()
updateEnrichRAnalysisNames()
updateFeatureDisplaySelect()
updateTSCANUICollapse()
# TODO: There are more things that need to be cleaned when uploading new
# dataset, including any plots, tables that are origined from the old
# datasets. Otherwise, errors may pop out when Shiny listens to the new
# object but cannot find the old result.
updateCollapse(session = session, "importUI",
open = "3. Data summary:",
close = "2. Create dataset:",
style = list("2. Create dataset:" = "success"))
callModule(module = nonLinearWorkflow, id = "nlw-import", parent = session,
qcf = TRUE)
}))
updateSeuratUIFromRDS <- function(inSCE){
if(!is.null(metadata(inSCE)$seurat$plots)){
showNotification(HTML("Computation from Seurat Report detected in the input object, therefore the toolkit will now populate the Seurat tab with computated data & plots for further inspection. Click on the button below to directly go the the Seurat tab of the toolkit now! <br><br>"),
type = "message", duration = 0, action = actionBttn(
inputId = "goToSeurat",
label = "Go to Seurat Curated Workflow",
style = "bordered",
color = "royal",
size = "s",
icon = icon("arrow-right")
), id = "goSeuratNotification")
#Normalize Data
shinyjs::enable(selector = "#SeuratUI > div[value='Normalize Data']")
updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
normalizeParams <- metadata(vals$counts)$seurat$sctk$report$normalizeParams
updateSelectInput(session, "normalization_method", selected = normalizeParams$normalizationMethod)
updateTextInput(session, "scale_factor", value = normalizeParams$scaleFactor)
#Scale Data
shinyjs::enable(selector = "div[value='Scale Data']")
updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
scaleParams <- metadata(vals$counts)$seurat$sctk$report$scaleParams
updateSelectInput(session, "model.use", selected = scaleParams$model)
#HVG
hvgParams <- metadata(vals$counts)$seurat$sctk$report$hvgParams
output$plot_hvg <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratHVG(vals$counts, labelPoints = hvgParams$labelPoints))
})
})
shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
updateSelectInput(session, "hvg_method", selected = hvgParams$hvgMethod)
updateTextInput(session, "hvg_no_features", value = hvgParams$hvgNumber)
updateTextInput(session, "hvg_no_features_view", value = hvgParams$labelPoints)
#DR
pcaParams <- metadata(vals$counts)$seurat$sctk$report$pcaParams
shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
shinyjs::show(selector = ".seurat_pca_plots")
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca")
)
), select = TRUE)
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
panel(heading = "Elbow Plot",
plotlyOutput(outputId = "plot_elbow_pca")
)
))
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
plotlyOutput(outputId = "plot_jackstraw_pca")
)
))
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionButton(inputId = "plot_heatmap_pca_button", "Plot")
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
)
)
))
# output$plot_pca <- renderPlotly({
# plotly::ggplotly(metadata(inSCE)$seurat$plots$pca)
# })
output$plot_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts, useReduction = "pca"))
})
})
#updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))
# output$plot_elbow_pca <- renderPlotly({
# metadata(inSCE)$seurat$plots$elbow
# })
#update parameters from seurat report
output$plot_elbow_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratElbow(inSCE = vals$counts))
})
})
output$pca_significant_pc_output <- renderText({
isolate({
paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", pcaParams$significant_PC," </span> </p> <hr>")
})
})
# output$plot_jackstraw_pca <- renderPlotly({
# plotly::ggplotly(metadata(inSCE)$seurat$plots$jackstraw)
# })
output$plot_jackstraw_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratJackStraw(vals$counts))
})
})
# output$plot_heatmap_pca <- renderPlot({
# metadata(inSCE)$seurat$plots$heatmap
# })
updateTextInput(session, "pca_no_components", value = pcaParams$nPCs)
updateMaterialSwitch(session, "pca_compute_jackstraw", value = TRUE)
updateNumericInput(session, "pca_significant_pc_counter", value = pcaParams$significant_PC)
pcHeatmapParams <- metadata(inSCE)$seurat$plots$heatmap
pcHeatmapParams$inSCE <- vals$counts
output$plot_heatmap_pca <- renderPlot({
isolate({
do.call("runSeuratHeatmap", pcHeatmapParams)
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
#2D-Embedding
shinyjs::enable(selector = "#SeuratUI > div[value='2D-Embedding']")
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
# output$plot_tsne <- renderPlotly({
# metadata(inSCE)$seurat$plots$tsne
# })
#
# output$plot_umap <- renderPlotly({
# metadata(inSCE)$seurat$plots$umap
# })
output$plot_tsne <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne"))
})
})
output$plot_umap <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap"))
})
})
#Clustering
clusterParams <- metadata(vals$counts)$seurat$sctk$report$clusterParams
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca_clustering")
)
), select = TRUE
)
output$plot_pca_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "pca", showLegend = TRUE))
})
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotlyOutput(outputId = "plot_tsne_clustering")
)
)
)
output$plot_tsne_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "tsne", showLegend = TRUE))
})
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotlyOutput(outputId = "plot_umap_clustering")
)
)
)
output$plot_umap_clustering <- renderPlotly({
plotly::ggplotly(plotSeuratReduction(vals$counts, useReduction = "umap", showLegend = TRUE))
})
shinyjs::show(selector = ".seurat_clustering_plots")
updateNumericInput(session, "resolution_clustering", value = clusterParams$resolution)
#Find Markers
shinyjs::enable(selector = "#SeuratUI > div[value='Find Markers']")
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
shinyjs::show(selector = ".seurat_findmarker_table")
shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
shinyjs::show(selector = ".seurat_findmarker_plots")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerRidgePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerFeaturePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerDotPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "findMarkerHeatmapPlot")
)
)
)
)
)
)
showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "Ridge Plot")
shinyjs::show(selector = ".seurat_findmarker_plots")
groupHeatmapParams <- metadata(vals$counts)$seurat$plots$groupHeatmapParams
groupHeatmapParams$inSCE <- vals$counts
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
do.call("plotSeuratGenes", groupHeatmapParams)
})
})
output$findMarkerHeatmapPlotFullTopText <- renderUI({
h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
})
updateSelectInput(session, "seuratFindMarkerSelectPhenotype", choices = colnames(colData(vals$counts)), selected = metadata(vals$counts)$seurat$plots$group)
vals$fts <- callModule(
module = filterTableServer,
id = "filterSeuratFindMarker",
dataframe = metadata(vals$counts)$seurat$plots$top9,
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
#Downstream Analysis
shinyjs::show(selector = "div[value='Downstream Analysis']")
updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
}
}
observeEvent(input$goToSeurat,{
updateTabsetPanel(session, "navbar",
selected = "Seurat")
removeNotification(id = "goSeuratNotification", session = session)
})
observeEvent(input$importFeatureDipSet, {
if (!is.null(vals$counts)) {
withBusyIndicatorServer("importFeatureDipSet", {
selected <- NULL
if (!stringr::word(input$importFeatureDispOpt, 1) == "Default") {
featureName <- word(input$importFeatureDispOpt, 1)
selected <- featureName
}
if (!stringr::word(input$importFeatureNamesOpt, 1) == "Default") {
featureID <- word(input$importFeatureNamesOpt, 1)
rownames(vals$counts) <- rowData(vals$counts)[[featureID]]
}
vals$counts <- setSCTKDisplayRow(vals$counts, selected)
updateFeatureDisplaySelect(selected = selected)
})
}
})
updateFeatureDisplaySelect <- function(selected = NULL, updateOptions = FALSE)
{
if (is.null(selected)) {
if (!is.null(vals$counts))
selected <- metadata(vals$counts)$featureDisplay
if (is.null(selected))
selected <- "Rownames (Default)"
}
updateSelectInput(session, "hvgPlotFeatureDisplay", selected = selected)
updateSelectInput(session, "fmHMFeatureDisplay", selected = selected)
updateSelectInput(session, "deHMrowLabel", selected = selected)
updateSelectInput(session, "deVolcFeatureDisplay", selected = selected)
updateSelectInput(session, "deVioLabel", selected = selected)
updateSelectInput(session, "deRegLabel", selected = selected)
updateSelectInput(session, "tscanDEFeatureDisplay", selected = selected)
updateSelectInput(session, "plotTSCANClusterDEG_featureDisplay",
selected = selected)
updateSelectInput(session, "plotTSCANDimReduceFeatures_featureDisplay",
selected = selected)
}
#-----------#
# Gene Sets ####
#-----------#
numGS <- reactiveValues(id_count = 0)
addToGSTable <- function(nameCol, locCol) {
numGS$id_count <- numGS$id_count + 1
id <- paste0("geneSet", numGS$id_count)
fluidRowStyle <- paste0(paste0("#", id), "{border-bottom: 1px solid #bababa; padding-top: .9%; padding-bottom: .5%}")
insertUI(
selector = "#newGSImport",
ui = fluidRow(
id = id,
tags$style(HTML(fluidRowStyle)),
column(3, nameCol),
column(9, locCol),
)
)
}
vals$defaultQCGS <- c("None" = "none",
"Human Mitochondrial Genes (Ensembl)" = "he",
"Human Mitochondrial Genes (Symbol)" = "hs",
"Mouse Mitochondrial Genes (Ensembl)" = "me",
"Mouse Mitochondrial Genes (Symbol)" = "ms")
cleanGSTable <- function() {
for (i in seq(numGS$id_count)) {
removeUI(
selector = paste0("#geneSet", i)
)
}
numGS$id_count <- 0
if (!is.null(vals$counts)) {
existGS <- sctkListGeneSetCollections(vals$counts)
if (length(existGS) > 0) {
for (i in existGS) {
addToGSTable(i, "SCE Object")
}
updateSelectInput(session, "gsExisting", choices = c("None", existGS))
names(existGS) <- existGS
updateSelectInput(session, "QCMgeneSets", choices =c("None", existGS),
selected = "None")
shinyjs::show(id = "gsAddToExisting", anim = FALSE)
} else {
shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
}
} else {
updateSelectInput(session, "gsExisting", choices = "None")
updateSelectInput(session, "QCMgeneSets", choices = "None")
shinyjs::hide(id = "gsAddToExisting", anim = FALSE)
}
}
handleGSPasteOption <- function(byParam) {
if (!nzchar(input$geneSetText)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else if ((!nzchar(input$gsCollectionNameText)) && (input$gsExisting == "None")) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
setList <- formatGeneSetList(input$geneSetText)
if (nzchar(input$gsCollectionNameText)) {
vals$counts <- importGeneSetsFromList(vals$counts,
setList,
by = byParam,
collectionName = input$gsCollectionNameText)
addToGSTable(input$gsCollectionNameText, "Paste-In")
} else if (input$gsExisting != "None") {
vals$counts <- importGeneSetsFromList(vals$counts,
setList,
by = byParam,
collectionName = input$gsExisting)
addToGSTable(input$gsExisting, "Paste-In")
}
}
}
observeEvent(input$uploadGS, withConsoleMsgRedirect(
msg = "Please wait while gene sets are being imported. See console log for progress.",
{
byParam = NULL
if (input$gsByParam != "None") {
byParam <- input$gsByParam
}
if (input$geneSetSourceChoice == "gsGMTUpload") {
if (is.null(input$geneSetGMT)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else if (!nzchar(input$gsCollectionNameGMT)){
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
vals$counts <- importGeneSetsFromGMT(vals$counts,
input$geneSetGMT$datapath,
by = byParam,
collectionName = input$gsCollectionNameGMT)
addToGSTable(input$gsCollectionNameGMT, input$geneSetGMT$datapath)
}
} else if (input$geneSetSourceChoice == "gsDBUpload") {
if (is.null(input$geneSetDB)) {
shinyjs::show(id = "gsUploadError", anim = FALSE)
} else {
shinyjs::hide(id = "gsUploadError", anim = FALSE)
vals$counts <- importGeneSetsFromMSigDB(vals$counts,
input$geneSetDB,
by = byParam)
for(i in input$geneSetDB){
# Handling multiple selections from the checkboxInput
addToGSTable(i, "Database")
}
}
} else if (input$geneSetSourceChoice == "gsMito") {
vals$counts <- importMitoGeneSet(vals$counts,
reference = input$geneSetMitoSpecies,
id = input$geneSetMitoID,
by = byParam,
collectionName = input$geneSetMitoName)
addToGSTable(input$geneSetMitoName, "SCTK Curated Geneset")
} else if (input$geneSetSourceChoice == "gsPasteUpload") {
handleGSPasteOption(byParam)
}
updateGeneSetSelection()
shinyjs::show(id = "gsAddToExisting", anim = FALSE)
}))
#----#
# QC #####
#----#
# Hide and show parameters for QC functions
shinyjs::onclick("QCMetrics", shinyjs::toggle(id = "QCMetricsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("decontX", shinyjs::toggle(id = "decontXParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("soupX", shinyjs::toggle(id = "soupXParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("scDblFinder", shinyjs::toggle(id = "scDblFinderParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("cxds", shinyjs::toggle(id = "cxdsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("bcds", shinyjs::toggle(id = "bcdsParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("cxds_bcds_hybrid", shinyjs::toggle(id = "cxds_bcds_hybridParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("scrublet", shinyjs::toggle(id = "scrubletParams",
anim = FALSE), add = TRUE)
shinyjs::onclick("doubletFinder", shinyjs::toggle(id = "doubletFinderParams",
anim = FALSE), add = TRUE)
qc_choice_list <- list("scDblFinder", "cxds", "bcds",
"cxds_bcds_hybrid", "decontX", "soupX", "QCMetrics", "scrublet", "doubletFinder")
# holds all the input ids for the QC algorithm parameters by algorithm name
qc_input_ids <- list(scDblFinder = list(nNeighbors="DCnNeighbors", simDoublets="DCsimDoublets"),
cxds = list(ntop="CXntop", binThresh="CXbinThresh", verb="CXverb", retRes="CXretRes"),#, estNdbl="CXestNdbl"),
bcds = list(ntop="BCntop", srat="BCsrat", nmax="BCnmax", verb="BCverb", retRes="BCretRes", varImp="BCvarImp"),#, estNdbl="BCestNdbl"),
cxds_bcds_hybrid = list(cxdsArgs=list(ntop="CX2ntop", binThresh="CX2binThresh", retRes="CX2retRes"),
bcdsArgs=list(ntop="BC2ntop", srat="BC2srat", nmax="BC2nmax", retRes="BC2retRes", varImp="BC2varImp"),
verb="CXBCverb"),#, estNdbl="CXBCestNdbl"),
decontX = list(maxIter="DXmaxIter", estimateDelta="DXestimateDelta", convergence="DXconvergence",
iterLogLik="DXiterLogLik", varGenes="DXvarGenes", dbscanEps="DXdbscanEps", verbose="DXverbose"),
soupX = list(cluster="soupXCluster", tfidfMin="soupXTfidfMin", soupQuantile="soupXQuantile", maxMarkers="soupXMaxMarkers", rhoMaxFDR="soupXRhoMaxFDR",
priorRho="soupXPriorRho", priorRhoStdDev="soupXPriorRhoStdDev", forceAccept="soupXForceAccept", adjustMethod="soupXAdjustMethod",
roundToInt="soupXRoundToInt", tol="soupXTol", pCut="soupXPCut"),
doubletFinder = list(seuratNfeatures="DFseuratNfeatures", seuratRes="DFseuratRes", formationRate="DFformationRate", verbose="DFverbose"),
scrublet = list(simDoubletRatio="SsimDoubletRatio", nNeighbors="SnNeighbors", minDist="SminDist", expectedDoubletRate="SexpectedDoubletRate",
stdevDoubletRate='SstdevDoubletRate', syntheticDoubletUmiSubsampling="SsyntheticDoubletUmiSubsampling",
useApproxNeighbors="SuseApproxNeighbors", distanceMetric="SdistanceMetric", getDoubletNeighborParents="SgetDoubletNeighborParents", minCounts="SminCounts",
minCells="SminCells", minGeneVariabilityPctl="SminGeneVariabilityPctl", logTransform="SlogTransform", meanCenter="SmeanCenter",
normalizeVariance="SnormalizeVariance", nPrinComps="SnPrinComps", tsneAngle="StsneAngle", tsnePerplexity="StsnePerplexity", verbose="Sverbose")
)
# to keep track of whether an algo has already been run
qc_algo_status = reactiveValues(scDblFinder=NULL, cxds=NULL, bcds=NULL, cxds_bcds_hybrid=NULL, decontX=NULL, soupX=NULL,
QCMetrics=NULL, scrublet=NULL, doubletFinder=NULL)
qc_plot_ids = reactiveValues(scDblFinder="DCplots", cxds="CXplots", bcds="BCplots", cxds_bcds_hybrid="CXBCplots", decontX="DXplots",
soupX="SoupXPlots", QCMetrics="QCMplots", scrublet="Splots", doubletFinder="DFplots")
# event handlers to open help pages for each qc algorithm
observeEvent(input$DXhelp, {
showModal(decontXHelpModal())
})
observeEvent(input$SoupXhelp, {
showModal(soupXHelpModal())
})
observeEvent(input$CXhelp, {
showModal(cxdsHelpModal())
})
observeEvent(input$BChelp, {
showModal(bcdsHelpModal())
})
observeEvent(input$CXBChelp, {
showModal(cxdsBcdsHybridHelpModal())
})
observeEvent(input$DFhelp, {
showModal(doubletFinderHelpModal())
})
observeEvent(input$Shelp, {
showModal(scrubletHelpModal())
})
observeEvent(input$DChelp, {
showModal(scDblFinderHelpModal())
})
observeEvent(input$QCMhelp, {
showModal(QCMHelpModal())
})
observeEvent(input$QCImportGS, {
showTab(inputId = "navbar",
target = "Import Gene Sets",
select = TRUE,
session = session)
})
# format the parameters for decontX
prepDecontXParams <- function(paramsList) {
inputIds <- qc_input_ids[["decontX"]]
dxParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
dxParams[[key]] = input[[inputIds[[key]]]]
}
# put in the delta params (c-bind the two priors)
dxParams[["delta"]] <- c(input$DXnativePrior, input$DXcontPrior)
# add to master params list
paramsList[["decontX"]] = dxParams
return(paramsList)
}
# format the parameters for SoupX
prepSoupXParams <- function(paramsList) {
inputIds <- qc_input_ids[["soupX"]]
soupXParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
soupXParams[[key]] = input[[inputIds[[key]]]]
}
soupXParams[["contaminationRange"]] <- c(input$soupXContRangeLow,
input$soupXContRangeHigh)
if (soupXParams[["cluster"]] == "None") {
soupXParams[["cluster"]] <- NULL
}
# add to master params list
paramsList[["decontX"]] = soupXParams
return(paramsList)
}
# format the parameters for doubletFinder
prepDoubletFinderParams <- function(paramsList) {
inputIds <- qc_input_ids[["doubletFinder"]]
dfParams <- list()
# put in all the params from the list (the straightforward ones)
for (key in names(inputIds)) {
dfParams[[key]] = input[[inputIds[[key]]]]
}
# put in the seuratPcs param (range from 1 to given value)
dfParams[["seuratPcs"]] <- 1:input$DFseuratPcs
# add to master params list
paramsList[["doubletFinder"]] = dfParams
return(paramsList)
}
qcInputExists <- function() {
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
return(TRUE)
}
}
return(FALSE)
}
updateQCPlots <- function() {
# get selected sample from run QC section
if (!is.null(vals$original)) {
qcSample <- input$qcSampleSelect
if (qcSample == "None") {
qcSample <- NULL
} else {
qcSample <- colData(vals$original)[,input$qcSampleSelect]
}
# build list of selected algos
algoList = list()
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
algoList <- c(algoList, algo)
}
}
# only run runUMAP if there are no reducedDimNames
# redDimName <- input$qcPlotRedDim
# show the tabs for the result plots output[[qc_plot_ids[[a]]]]
showQCResTabs(vals, algoList, qc_algo_status, qc_plot_ids)
arrangeQCPlots(vals$original, input, output, algoList,
colData(vals$original)[[input$qcSampleSelect]], qc_plot_ids,
qc_algo_status, input$QCUMAPName)
uniqueSampleNames = unique(colData(vals$original)[[input$qcSampleSelect]])
for (algo in algoList) {
qc_algo_status[[algo]] <- list(self="done")
if (length(uniqueSampleNames) > 1) {
for (s in uniqueSampleNames) {
qc_algo_status[[algo]][[s]] = TRUE
}
}
}
}
}
observeEvent(input$runQC, withConsoleMsgRedirect(
msg = "Please wait while QC metrics are being calculated. See console log for progress.",
{
if (!qcInputExists()) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noSelected", tags$b("Please select at least one algorithm.", style = "color: red;"))
)
} else if (is.null(vals$original)) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noSCE", tags$b("Please upload a sample first.", style = "color: red;"))
)
} else if (is.null(input$qcAssaySelect)) {
insertUI(
selector = "#qcPageErrors",
ui = wellPanel(id = "noQCAssay", tags$b("Please select an assay.", style = "color: red;"))
)
} else {
removeUI(
selector = "#noSelected"
)
removeUI(
selector = "#noSCE"
)
removeUI(
selector = "#noQCAssay"
)
useAssay <- input$qcAssaySelect
qcSample <- colData(vals$original)[,input$qcSampleSelect]
if (length(qcSample)==1 && qcSample == "None") {
qcSample <- NULL
}
# Handle mitochondrial gene set selection
mgsRef <- NULL
mgsId <- NULL
mgsLoc <- NULL
if (input$QCMito != "None") {
if (input$QCMito == "he") {
# Import Human Mito Ensembl
mgsRef <- "human"
mgsId <- "ensembl"
} else if (input$QCMito == "hs") {
# Import Human Mito Symbol
mgsRef <- "human"
mgsId <- "symbol"
} else if (input$QCMito == "me") {
# Import Mouse Mito Ensembl
mgsRef <- "mouse"
mgsId <- "ensembl"
} else if (input$QCMito == "ms") {
# Import Mouse Mito Symbol
mgsRef <- "mouse"
mgsId <- "symbol"
}
mgsLoc <- "rownames"
}
# Handle another genesetCollection selection
qcCollName <- NULL
if (input$QCMgeneSets != "None") {
qcCollName <- input$QCMgeneSets
}
algoList = list()
paramsList <- list()
for (algo in qc_choice_list) {
if (isTRUE(input[[algo]])) {
algoList <- c(algoList, algo)
# use the specific prep functions for decontX, SoupX and doubletFinder
if (algo == "decontX") {
paramsList <- prepDecontXParams(paramsList)
next
}
if (algo == "soupX") {
paramsList <- prepSoupXParams(paramsList)
next
}
if (algo == "doubletFinder") {
paramsList <- prepDoubletFinderParams(paramsList)
next
}
# everything else can go through the rest of the loop
inputIds <- qc_input_ids[[algo]]
algoParams <- list()
for (key in names(inputIds)) {
if(typeof(inputIds[[key]]) == "list") {
paramSubList <- list()
for (key2 in names(inputIds[[key]])) {
paramSubList[[key2]] <- input[[inputIds[[key]][[key2]]]]
}
algoParams[[key]] = paramSubList
} else {
algoParams[[key]] = handleEmptyInput(input[[inputIds[[key]]]])
}
}
paramsList[[algo]] = algoParams
}
}
# run selected cell QC algorithms
vals$original <- runCellQC(inSCE = vals$original,
algorithms = algoList,
sample = qcSample,
collectionName = qcCollName,
mitoRef = mgsRef,
mitoIDType = mgsId,
mitoGeneLocation = mgsLoc,
useAssay = input$qcAssaySelect,
paramsList = paramsList)
# Only copy the newly generated colData variables to vals$counts, but
# not replacing the vals$counts. vals$counts might have already become
# a subset.
vals$counts <- passQCVar(vals$original, vals$counts, algoList)
updateColDataNames()
updateAssayInputs()
# redDimList <- strsplit(reducedDimNames(vals$original), " ")
# run runUMAP if doublet/ambient RNA detection conducted
#umap generated during soupX, skip for now
if (length(intersect(c("scDblFinder", "cxds", "bcds",
"cxds_bcds_hybrid", "decontX", #"soupX",
"scrublet", "doubletFinder"), algoList)) > 0) {
message(paste0(date(), " ... Running 'UMAP'"))
vals$original <- runUMAP(inSCE = vals$original,
sample = qcSample,
useAssay = input$qcAssaySelect,
useReducedDim = NULL,
nNeighbors = input$UnNeighbors,
nIterations = input$UnIterations,
alpha = input$Ualpha,
minDist = input$UminDist,
spread = input$Uspread,
initialDims = input$UinitialDims,
reducedDimName = input$QCUMAPName,
seed = input$Useed)
}
message(paste0(date(), " ... QC Complete"))
updateQCPlots()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-qcf", parent = session,
nbc = TRUE, cw = TRUE, cv = TRUE)
}
delay(500, removeNotification(id = "qcNotification"))
}))
#-----------#
# FILTERING #####
#-----------#
shinyjs::onclick("colGT", shinyjs::toggle(id = "filterThreshGT",
anim = FALSE), add = TRUE)
shinyjs::onclick("colLT", shinyjs::toggle(id = "filterThreshLT",
anim = FALSE), add = TRUE)
filteringParams <- reactiveValues(params = list(), id_count = 0)
rowFilteringParams <- reactiveValues(params = list(), id_count = 0)
observeEvent(input$addFilteringParam, {
if (!is.null(vals$original)) {
showModal(filteringModal(colNames = names(colData(vals$original))))
}
})
observeEvent(input$addRowFilteringParam, {
if (!is.null(vals$original) &&
!is.null(names(assays(vals$original)))) {
showModal(rowFilteringModal(assayInput = names(assays(vals$original))))
}
})
observeEvent(input$filterColSelect, {
# prep the modal - remove the threshold div and hide the categorical option
shinyjs::hide("convertFilterType")
removeUI(selector = "#newThresh")
removeUI(selector = "div:has(>> #convertToCat)")
# check if column contains numerical values
isNum <- is.numeric(vals$original[[input$filterColSelect]][0])
if (length(vals$original[[input$filterColSelect]]) > 0) {
if (isTRUE(isNum)) {
# (from partials) insertUI for choosing greater than and less than params
addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
# if less than 25 unique categories, give categorical option
if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
insertUI(
selector = "#convertFilterType",
ui = checkboxInput("convertToCat", "Convert to categorical filter?")
)
shinyjs::show("convertFilterType")
}
} else { # if non-numerical values, create checkbox input
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh",
checkboxGroupInput("filterThresh", "Please select which columns to keep:",
choices = as.vector(unique(vals$original[[input$filterColSelect]])),
),
)
)
}
} else { # if no values in column, show error
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh", tags$b("This column does not have any filtering criteria", style = "color: red;"))
)
}
})
observeEvent(input$convertToCat, {
if (!is.null(input$filterColSelect)) {
removeUI(selector = "#newThresh")
if (input$convertToCat) {
insertUI(
selector = "#filterCriteria",
ui = tags$div(id="newThresh",
checkboxGroupInput("filterThresh", "Please select which columns to keep:",
choices = as.vector(unique(vals$original[[input$filterColSelect]])),
)
)
)
} else {
addFilteringThresholdOptions(vals$original[[input$filterColSelect]])
if (length(unique(vals$original[[input$filterColSelect]])) < 25) {
shinyjs::show("convertFilterType")
}
}
}
})
observeEvent(input$filterAssaySelect, {
removeUI(selector = "#newThresh")
insertUI(
selector = "#rowFilterCriteria",
ui = tags$div(id="newThresh",
numericInput("filterThreshX", "Keep features with this many counts:", 0),
numericInput("filterThreshY", "In at least this many cells:", 0),
)
)
})
observeEvent(input$filtModalOK, {
if (is.null(input$filterThresh) && is.null(input$filterThreshGT) && is.null(input$filterThreshLT)) {
showModal(filteringModal(failed=TRUE, colNames = names(colData(vals$original))))
} else {
id <- paste0("filteringParam", filteringParams$id_count)
# figure out which options the user selected
criteriaGT <- NULL
criteriaLT <- NULL
categoricalCol = FALSE
if (isTRUE(input$colGT)) {
criteriaGT = input$filterThreshGT
}
if (isTRUE(input$colLT)) {
criteriaLT = input$filterThreshLT
}
if (!is.null(input$filterThresh)) {
categoricalCol = TRUE
}
if (isTRUE(input$colLT) && isTRUE(input$colGT)) {
if (criteriaGT > criteriaLT) {
insertUI(
selector = "#filterCrErrors",
ui = wellPanel(id = "voidRange",
tags$b("Please set a valid range.",
style = "color: red;"))
)
return()
}
}
# new row in parameters table
addToColFilterParams(name = input$filterColSelect,
categorial = categoricalCol,
criteria = input$filterThresh,
criteriaGT = criteriaGT,
criteriaLT = criteriaLT,
id = id,
paramsReactive = filteringParams)
threshStr <- ""
if (isTRUE(categoricalCol)) {
threshStr <- paste(input$filterThresh, collapse = ', ')
} else {
if (is.null(criteriaGT)) {
threshStr <- sprintf("< %.5f", input$filterThreshLT)
} else if (is.null(criteriaLT)) {
threshStr <- sprintf("> %.5f", input$filterThreshGT)
} else {
threshStr <- sprintf("> %.5f & < %.5f", input$filterThreshGT, input$filterThreshLT)
}
}
make3ColTableRow("#newFilteringParams", id, input$filterColSelect, threshStr)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in filteringParams$params) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
filteringParams$params <- filteringParams$params[toRemove]
})
removeModal()
}
})
observeEvent(input$rowFiltModalOK, {
if ((is.null(input$filterThreshX)) || (is.null(input$filterThreshY)) || (is.null(input$filterAssaySelect))) {
showModal(rowFilteringModal(failed=TRUE, assayInput = names(assays(vals$original))))
} else {
id <- paste0("rowFilteringParam", rowFilteringParams$id_count)
# new row in parameters table
threshStr <- sprintf("> %i counts in > %i cells", input$filterThreshX, input$filterThreshY)
addToRowFilterParams(input$filterAssaySelect, input$filterThreshX, input$filterThreshY, id, rowFilteringParams)
make3ColTableRow("#newRowFilteringParams", id, input$filterAssaySelect, threshStr)
observeEvent(input[[paste0("remove", id)]],{
removeUI(
selector = paste0("#", id)
)
toRemove <- vector()
for (entry in rowFilteringParams$params) {
if (entry$id == id) {
toRemove <- c(toRemove, FALSE)
} else {
toRemove <- c(toRemove, TRUE)
}
}
rowFilteringParams$params <- rowFilteringParams$params[toRemove]
})
removeModal()
}
})
observeEvent(input$clearAllFilters, {
for (entry in filteringParams$params) {
removeUI(selector = paste0("#", entry$id))
}
filteringParams$params <- list()
})
observeEvent(input$clearAllRowParams, {
for (entry in rowFilteringParams$params) {
removeUI(selector = paste0("#", entry$id))
}
rowFilteringParams$params <- list()
})
filterSCE <- function(inSCE, colFilter, rowFilter) {
if (!is.null(colFilter)) {
# handle column filtering (pull out the criteria strings first)
colInput <- formatFilteringCriteria(colFilter$params)
if (length(colInput) > 0) {
inSCE <- subsetSCECols(inSCE, colData = colInput)
}
}
if (!is.null(rowFilter)) {
# handle row filtering (enter information as rows first, then pull out
# criteria strings)
rowInput <- formatFilteringCriteria(rowFilter$params)
if (length(rowInput) > 0) {
inSCE <- addRowFiltersToSCE(inSCE, rowFilter)
temp <- subsetSCERows(inSCE, rowData = rowInput, returnAsAltExp = FALSE)
if (nrow(temp) == 0) {
stop("This filter will clear all rows. Filter has not been applied.")
} else {
inSCE <- temp
}
}
}
return(inSCE)
}
observeEvent(input$filterSCE, withConsoleMsgRedirect(
msg = "Please wait while data is being filtered. See console log for progress.",
{
vals$counts <- filterSCE(vals$original, filteringParams, rowFilteringParams)
shinyjs::show(id="filteringSummary")
updateColDataNames()
updateReddimInputs()
updateFeatureAnnots()
updateAssayInputs()
# TODO: When new subset is being created and maybe replacing previous
# vals$counts, please find if any of the downstream UI need to be updated
# Show downstream analysis options
shinyjs::show(selector = ".nlw-qcf")
}))
#Render summary table
output$beforeFiltering <- renderTable({
req(vals$original)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$original,
useAssay = NULL,
sampleVariableName = sampleVar)
}, striped = TRUE, border = TRUE, align = "c", spacing = "l")
output$afterFiltering <- renderTable({
req(vals$counts)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$counts,
useAssay = NULL,
sampleVariableName = sampleVar)
}, striped = TRUE, border = TRUE, align = "c", spacing = "l")
#Render summary table
output$summarycontents <- DT::renderDataTable({
req(vals$counts)
if ("sample" %in% names(colData(vals$counts))) {
sampleVar <- "sample"
} else if ("Sample" %in% names(colData(vals$counts))) {
sampleVar <- "Sample"
} else {
sampleVar <- NULL
}
# Setting 'useAssay=NULL' assumes that the first assay is the one to count
singleCellTK::summarizeSCE(inSCE = vals$counts,
useAssay = NULL,
sampleVariableName = sampleVar)
})
observeEvent(input$filteredSample, {
output$filterSampleOptions <- renderUI({
isolate({
if (input$filteredSample != "none")({
if (length(unique(colData(vals$counts)[, input$filteredSample])) < 100){
L <- vector("list", 3)
L[[1]] <- renderText("Select samples to keep")
L[[2]] <- wellPanel(style = "overflow-y:scroll; max-height: 100px",
list(checkboxGroupInput("filterSampleChoices",
label = NULL,
choices = unique(colData(vals$counts)[, input$filteredSample]))),
tags$h5(tags$i("Note: the Reset button is in 'Delete Outliers' tab above."))
)
L[[3]] <- list(withBusyIndicatorUI(actionButton("runFilterSample", "Filter")))
return(L)
} else {
L <- list(renderText("Annotation must have fewer than 100 options"))
return(L)
}
}) else {
L <- list()
}
})
})
})
# Delete Data ####
output$reducedDimsList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts) &&
length(names(reducedDims(vals$counts))) > 0){
panel(heading = "ReducedDims",
checkboxGroupInput(
inputId = "checkboxRedDimToRemove",
label = NULL,
choices = names(reducedDims(vals$counts))
)
)
}
})
output$assaysList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)){
panel(heading = "Assays",
checkboxGroupInput(
inputId = "checkboxAssaysToRemove",
label = NULL,
choices = assayNames(vals$counts)
)
)
}
})
output$rowDataList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(colnames(rowData(vals$counts))) > 0){
panel(heading = "Row Annotation",
checkboxGroupInput(
inputId = "checkboxRowDataToRemove",
label = NULL,
choices = colnames(rowData(vals$counts))
)
)
}
})
output$colDataList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(colnames(colData(vals$counts))) > 0){
panel(heading = "Column Annotation",
checkboxGroupInput(
inputId = "checkboxColDataToRemove",
label = NULL,
choices = colnames(colData(vals$counts))
)
)
}
})
output$altExpList <- renderUI({
req(vals$counts)
if (!is.null(vals$counts)
&& length(altExpNames(vals$counts)) > 0){
panel(heading = "Subsets",
checkboxGroupInput(
inputId = "checkboxAltExpToRemove",
label = NULL,
choices = altExpNames(vals$counts)
)
)
}
})
observeEvent(input$delRedDim, withConsoleMsgRedirect(
msg = "Please wait while selected data is being removed. See console log for progress.",
{
req(vals$counts)
if(length(input$checkboxAssaysToRemove) > 0){
for(i in seq(input$checkboxAssaysToRemove)){
expData(vals$counts, input$checkboxAssaysToRemove[i]) <- NULL
vals$counts <- expDeleteDataTag(vals$counts, input$checkboxAssaysToRemove[i])
message(paste0(date(), " ... Removed '", input$checkboxAssaysToRemove[i], "' assay."))
}
}
if(length(input$checkboxRedDimToRemove) > 0){
for(i in seq(input$checkboxRedDimToRemove)){
reducedDim(vals$counts, input$checkboxRedDimToRemove[i]) <- NULL
message(paste0(date(), " ... Removed '", input$checkboxRedDimToRemove[i], "' redDim."))
}
}
if(length(input$checkboxRowDataToRemove) > 0){
for(i in seq(input$checkboxRowDataToRemove)){
rowData(vals$counts)[[input$checkboxRowDataToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxRowDataToRemove[i], "' feature annotation."))
}
}
if(length(input$checkboxColDataToRemove) > 0){
for(i in seq(input$checkboxColDataToRemove)){
colData(vals$counts)[[input$checkboxColDataToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxColDataToRemove[i], "' sample annotation."))
}
}
if(length(input$checkboxAltExpToRemove) > 0){
for(i in seq(input$checkboxAltExpToRemove)){
altExps(vals$counts)[[input$checkboxAltExpToRemove[i]]] <- NULL
message(paste0(date(), " ... Removed '", input$checkboxAltExpToRemove[i], "' subset."))
}
}
updateAssayInputs()
updateReddimInputs()
updateFeatureAnnots()
updateColDataNames()
}))
# Normalization ####
observeEvent(input$customNormalizeAssayMethodSelect, {
if(input$customNormalizeAssayMethodSelect == "LogNormalize"
|| input$customNormalizeAssayMethodSelect == "CLR"
|| input$customNormalizeAssayMethodSelect == "SCTransform"
|| input$customNormalizeAssayMethodSelect == "logNormCounts"){
updateAwesomeCheckbox(
session = session,
inputId = "customNormalizeOptionsTransform",
value = FALSE
)
}
})
output$normalizationDataTagUI <- renderUI({
req(vals$counts)
tag <- ""
if(input$normalizeAssayMethodSelect != "custom"){
if(input$normalizeAssayMethodSelect
%in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts", "NormalizeTotal")){
tag <- "transformed"
}
else{
tag <- "normalized"
}
if(input$normalizationScale){
tag <- "scaled"
}
}
else{
if(input$customNormalizeOptionsNormalize){
if(input$customNormalizeAssayMethodSelect
%in% c("LogNormalize", "SCTransform", "CLR", "logNormCounts")){
tag <- "transformed"
}
else{
tag <- "normalized"
}
}
if(input$customNormalizeOptionsTransform){
tag <- "transformed"
}
if(input$customNormalizeOptionsScale){
tag <- "scaled"
}
}
return(tag)
})
output$normalizationNormalizeSelectedMethodUI <- renderUI({
req(vals$counts)
if(input$normalizeAssayMethodSelect != "custom"){
h5(input$normalizeAssayMethodSelect)
}
else{
NULL
}
})
observeEvent(input$modifyAssay, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
if (!(input$modifyAssaySelect %in% names(assays(vals$counts)))) {
stop("Assay does not exist!")
} else if (input$modifyAssayOutname == "") {
stop("Assay name cannot be empty!")
} else if (input$modifyAssayOutname %in% names(assays(vals$counts))) {
stop("Assay name already exists! Use another assay name!")
} else if(is.na(input$trimUpperValueAssay)
|| is.na(input$trimLowerValueAssay)){
stop("Upper or lower trim value cannot be empty!")
} else {
checkedOptions <- c(input$customNormalizeOptionsNormalize,
input$customNormalizeOptionsTransform,
input$customNormalizeOptionsPsuedocounts,
input$customNormalizeOptionsScale,
input$customNormalizeOptionsTrim)
if(!any(checkedOptions)){
stop("Must select at least one option!")
}
#Setting initial parameters
normalizeMethod <- NULL
transformMethod <- NULL
pseudocountsBefore <- NULL
pseudocountsAfter <- NULL
doScale <- input$customNormalizeOptionsScale
trimOptions <- NULL
if(input$customNormalizeOptionsNormalize)
normalizeMethod <- input$customNormalizeAssayMethodSelect
if(input$customNormalizeOptionsTransform)
transformMethod <- input$customNormalizeTransformOptions
if(input$customNormalizePseudoOptionsBefore)
pseudocountsBefore <- input$customNormalizePseudoValueBefore
if(input$customNormalizePseudoOptionsAfter)
pseudocountsAfter <- input$customNormalizePseudoValueAfter
if(input$customNormalizeOptionsTrim)
trimOptions <- c(input$trimUpperValueAssay, input$trimLowerValueAssay)
outAssayName <- input$modifyAssayOutname
useAssay <- input$modifyAssaySelect
args <- list(
inSCE = vals$counts,
useAssay = useAssay,
outAssayName = outAssayName,
normalizationMethod = normalizeMethod,
scale = doScale,
transformation = transformMethod,
pseudocountsBeforeNorm = pseudocountsBefore,
pseudocountsBeforeTransform = pseudocountsAfter,
trim = trimOptions
)
message(paste0(date(), " ... Starting normalization/transformation with selected assay: '", useAssay, "'."))
vals$counts <- do.call("runNormalization", args)
message(paste0(date(), " ... Ended normalization/transformation."))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
dr = TRUE, fs = TRUE)
}
}
))
observeEvent(input$normalizeAssay, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
if(!(input$normalizeAssaySelect %in% expDataNames(vals$counts))){
stop("Selected assay does not exist!")
} else if(input$normalizeAssayOutname == ""){
stop("Assay Name cannot be empty!")
} else if(input$normalizeAssayOutname %in% expDataNames(vals$counts)){
stop("Your selected Assay Name already exists! Try another Assay Name!")
} else if(input$normalizeAssaySelect == ""){
stop("Please select an assay before proceeding with normalization!")
} else if(is.na(as.numeric(input$normalizationScaleFactor))){
stop("Scaling factor must be a numeric non-empty value!")
} else{
#Setting initial parameters
normalizeMethod <- input$normalizeAssayMethodSelect
doScale <- input$normalizationScale
trimOptions <- NULL
scaleFactor <- input$normalizationScaleFactor
if(doScale && input$normalizationTrim)
trimOptions <- c(input$normalizationTrimUpper,
input$normalizationTrimLower)
outAssayName <- input$normalizeAssayOutname
useAssay <- input$normalizeAssaySelect
args <- list(
inSCE = vals$counts,
useAssay = useAssay,
outAssayName = outAssayName,
normalizationMethod = normalizeMethod,
scale = doScale,
seuratScaleFactor = scaleFactor,
trim = trimOptions
)
message(date(), " ... Starting normalization with selected assay: '",
useAssay, "'.")
vals$counts <- do.call("runNormalization", args)
message(paste0(date(), " ... Ended normalization."))
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-nbc", parent = session,
dr = TRUE, fs = TRUE)
}
}
))
observeEvent(input$normalizeAssayMethodSelect, {
if(input$normalizeAssayMethodSelect == "LogNormalize") {
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratLogNormalize")
} else if(input$normalizeAssayMethodSelect == "CLR"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratCLR")
} else if(input$normalizeAssayMethodSelect == "RC"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratRC")
} else if(input$normalizeAssayMethodSelect == "CPM"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScaterCPMCounts")
} else if(input$normalizeAssayMethodSelect == "logNormCounts"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScaterLogNormCounts")
} else if(input$normalizeAssayMethodSelect == "SCTransform"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "SeuratSCTransform")
} else if(input$normalizeAssayMethodSelect == "NormalizeTotal"){
updateTextInput(session = session, inputId = "normalizeAssayOutname",
value = "ScanpyNormalizeTotal")
}
})
#-----------------------------------------------------------------------------
# Page 3: dimRed ####
#-----------------------------------------------------------------------------
output$dimRedNameUI <- renderUI({
defaultText <- paste(input$dimRedAssaySelect, input$dimRedPlotMethod,
sep = '_')
textInput('dimRedNameInput', "reducedDim Name:", defaultText)
})
output$dimRedNameUI_tsneUmap <- renderUI({
defaultText <- paste(input$dimRedAssaySelect_tsneUmap, input$dimRedPlotMethod_tsneUmap,
sep = '_')
textInput('dimRedNameInput_tsneUmap', "reducedDim Name:", defaultText)
})
observeEvent(input$updateHeatmap_dimRed, {
req(vals$counts)
if (!is.null(input$picker_dimheatmap_components_dimRed)) {
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plot_heatmap_dimRed <- renderPlot({
isolate({
singleCellTK:::.plotHeatmapMulti(
plots = vals$counts@metadata$seurat$heatmap_dimRed,
components = input$picker_dimheatmap_components_dimRed,
nCol = input$slider_dimheatmap_dimRed)
})
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plot_heatmap_dimRed <- renderPlot({
isolate({
singleCellTK:::.plotHeatmapMulti(
plots = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed,
components = input$picker_dimheatmap_components_dimRed,
nCol = input$slider_dimheatmap_dimRed)
})
})
}
}
session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
})
observeEvent(input$closeDropDownDimRedHeatmap, {
session$sendCustomMessage("close_dropDownDimRedHeatmap", "")
})
observeEvent(input$runDimred, withConsoleMsgRedirect(
msg = "Please wait while dimensionality reduction is being computed. See console log for progress.",
{
req(vals$counts)
vals$runDimred$dimRedAssaySelect <- input$dimRedAssaySelect
if (vals$runDimred$dimRedAssaySelect %in% altExpNames(vals$counts)) {
dimRedUseAltExp <- vals$runDimred$dimRedAssaySelect
} else {
dimRedUseAltExp <- NULL
}
if (input$dimRedNameInput == "" || is.null(input$dimRedNameInput)){
stop("Please enter a reducedDim name")
}
dimrednamesave <- gsub(" ", "_", input$dimRedNameInput)
if (dimrednamesave %in% reducedDimNames(vals$counts)) {
stop("Specified reducedDim name already exist")
}
if (is.na(input$dimRedNumberDims) ||
input$dimRedNumberDims < 2) {
stop("Must specify a valid number of components for output")
}
useFeatureSubset <- input$dimRedHVGSelect
if (input$dimRedHVGSelect == "None") {
useFeatureSubset <- NULL
}
seed <- input$seed_dimRed
if (is.na(input$seed_dimRed)) {
seed <- NULL
}
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = vals$runDimred$dimRedAssaySelect,
useAltExp = dimRedUseAltExp,
method = input$dimRedPlotMethod,
useFeatureSubset = useFeatureSubset,
scale = input$dimRedScale,
nComponents = input$dimRedNumberDims,
reducedDimName = dimrednamesave,
seed = seed)
updateReddimInputs()
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
cl = TRUE, cv = TRUE)
message(paste0(date(), " ... Ending Dimensionality Reduction."))
redDim <- reducedDim(vals$counts, dimrednamesave)
if(input$dimRedPlotMethod == "scaterPCA"){
new_pca <- CreateDimReducObject(
embeddings = redDim,
assay = "RNA",
loadings = attr(redDim, "rotation"),
stdev = as.numeric(attr(redDim, "percentVar")),
key = "PC_")
}
else if(input$dimRedPlotMethod == "scanpyPCA"){
new_pca <- CreateDimReducObject(
embeddings = redDim,
assay = "RNA",
loadings = attr(redDim, "rotation"),
stdev = as.numeric(attr(redDim, "percentVar")),
key = "PC_")
}
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Component Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Elbow Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "Heatmap Plot")
removeTab(inputId = "dimRedPCAICA_plotTabset", target = "JackStraw Plot")
shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
if(input$computeElbowPlot
&& input$dimRedPlotMethod != "seuratICA"){
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Elbow Plot",
panel(
#heading = "Elbow Plot",
plotlyOutput(outputId = "plotDimRed_elbow")
)
),
select = TRUE
)
message(paste0(date(), " ... Generating Elbow Plot."))
if (input$dimRedPlotMethod == "seuratPCA"){
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = vals$counts, )
})
} else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]])
})
}
} else {
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = vals$counts,
externalReduction = new_pca)
})
} else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
output$plotDimRed_elbow <- renderPlotly({
plotSeuratElbow(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
externalReduction = new_pca)
})
}
}
}
if(input$computeHeatmapPlot){
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Heatmap Plot",
tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedHeatmap', function(x){
$('html').click();
});"),
panel(
fluidRow(
column(4, dropdown(
fluidRow(actionBttn(inputId = "closeDropDownDimRedHeatmap", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
selectizeInput(inputId = "picker_dimheatmap_components_dimRed",
label = "Select principal components to plot:",
choices = c(),
multiple = TRUE),
numericInput(
inputId = "slider_dimheatmap_dimRed",
label = "Number of columns for the plot: ",
min = 1,
max = 4,
value = 3
),
actionBttn(
inputId = "updateHeatmap_dimRed",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
),
inputId = "dropDownDimRedHeatmap",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(6, fluidRow(h6("Heatmaps of the top features correlated with each selected component"), align = "center"))
),
hr(),
br(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "plot_heatmap_dimRed"),
options = list(maxWidth = 700)
)
)
)
)
message(paste0(date(), " ... Generating Heatmaps."))
if (input$dimRedPlotMethod == "seuratPCA") {
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "pca"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "pca"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
else if(input$dimRedPlotMethod == "seuratICA"){
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "ica"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
reduction = "ica"
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
else{
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
externalReduction = new_pca
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(vals$counts@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[input$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed <- singleCellTK::computeHeatmap(
inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
useAssay = input$dimRedAssaySelect,
dims = 1:input$dimRedNumberDims,
nfeatures = input$dimRedNFeaturesHeatmap,
externalReduction = new_pca
)
output$plot_heatmap_dimRed <- renderPlot({
singleCellTK:::.plotHeatmapMulti(altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]]@metadata$seurat$heatmap_dimRed, seq(6), 3)
})
}
}
compPrefix <- "PC"
if(input$dimRedPlotMethod == "seuratICA"){
compPrefix <- "IC"
}
compChoices <- rep(paste0(compPrefix, seq(input$dimRedNumberDims)))
updateSelectizeInput(
session = session,
inputId = "picker_dimheatmap_components_dimRed",
choices = compChoices,
selected = compChoices[seq(6)]
)
}
appendTab(
inputId = "dimRedPCAICA_plotTabset",
tabPanel(
title = "Component Plot",
panel(
tags$script("Shiny.addCustomMessageHandler('close_dropDownDimRedComponentPlot', function(x){$('html').click();});"),
fluidRow(
column(
4,
dropdown(
fluidRow(
column(
12,
fluidRow(
actionBttn(inputId = "closeDropDownDimRedComponentPlot",
label = NULL, style = "simple",
color = "danger",
icon = icon("times"), size = "xs"),
align = "right"),
selectizeInput(
inputId = "plotDimRed_pca_selectRedDim",
label = "Select reducedDim:",
choices = reducedDimNames(vals$counts)
),
numericInput(inputId = "plotDimRed_pca_dimX",
label = "Select component for X-axis:",
value = 1),
numericInput(inputId = "plotDimRed_pca_dimY",
label = "Select component for Y-axis:",
value = 2),
actionBttn(
inputId = "updateRedDimPlot_pca",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
),
inputId = "dropDownDimRedComponentPlot",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(
6,
fluidRow(h6("Scatterplot of cells on selected components from a dimensionality reduction"), align = "center"))
),
hr(),
br(),
plotlyOutput(outputId = "plotDimRed_pca")
)
)
)
message(paste0(date(), " ... Plotting PCA/ICA."))
output$plotDimRed_pca <- renderPlotly({
plotly::ggplotly(
plotDimRed(
inSCE = vals$counts,
useReduction = dimrednamesave,
xAxisLabel = paste0(input$dimRedPlotMethod, "_1"),
yAxisLabel = paste0(input$dimRedPlotMethod, "_2"))
)
})
if(input$computeJackstrawPlot
&& input$dimRedPlotMethod != "seuratICA"){
appendTab(inputId = "dimRedPCAICA_plotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_jackstraw_dimRed"))
)
))
if (input$dimRedPlotMethod == "seuratPCA"){
message(paste0(date(), " ... Generating JackStraw Plot."))
if(vals$runDimred$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = vals$counts, dims = input$dimRedNumberDims)
})
}
else if(vals$runDimred$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
useAssay = vals$runDimred$dimRedAssaySelect,
dims = input$dimRedNumberDims)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]], dims = input$dimRedNumberDims)
})
}
}
else{
message(paste0(date(), " ... Generating JackStraw Plot."))
if(input$dimRedAssaySelect %in% assayNames(vals$counts)){
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims,
externalReduction = new_pca)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = vals$counts,
dims = input$dimRedNumberDims)
})
}
else if(input$dimRedAssaySelect %in% expDataNames(vals$counts)){
altExps(vals$counts)[[input$dimRedAssaySelect]] <- runSeuratJackStraw(inSCE = altExps(vals$counts)[[input$dimRedAssaySelect]],
useAssay = input$dimRedAssaySelect,
dims = input$dimRedNumberDims,
externalReduction = new_pca)
output$plot_jackstraw_dimRed <- renderPlot({
plotSeuratJackStraw(inSCE = altExps(vals$counts)[[vals$runDimred$dimRedAssaySelect]],
dims = input$dimRedNumberDims)
})
}
}
}
}
))
observeEvent(input$updateRedDimPlot_pca,{
req(vals$counts)
output$plotDimRed_pca <- renderPlotly({
isolate({
plotly::ggplotly(
plotDimRed(
inSCE = vals$counts,
useReduction = input$plotDimRed_pca_selectRedDim,
xDim = input$plotDimRed_pca_dimX,
yDim = input$plotDimRed_pca_dimY,
xAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimX),
yAxisLabel = paste0(input$dimRedPlotMethod, "_", input$plotDimRed_pca_dimY))
)
})
})
session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
})
observeEvent(input$closeDropDownDimRedComponentPlot, {
req(vals$counts)
session$sendCustomMessage("close_dropDownDimRedComponentPlot", "")
})
observeEvent(input$dimRedAssaySelect_tsneUmap, {
req(vals$counts)
if (!is.null(input$dimRedAssaySelect_tsneUmap)) {
if (input$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
shinyjs::disable("reductionMethodUMAPTSNEDimRed")
shinyjs::disable("logNorm_tsneUmap")
shinyjs::disable("hvg_tsneUmap")
shinyjs::disable("scale_tsneUmap")
shinyjs::disable("pca_tsneUmap")
updateCheckboxInput(session, "logNorm_tsneUmap", value = FALSE)
updateCheckboxInput(session, "scale_tsneUmap", value = FALSE)
updateCheckboxInput(session, "pca_tsneUmap", value = FALSE)
} else {
shinyjs::enable("reductionMethodUMAPTSNEDimRed")
shinyjs::enable("logNorm_tsneUmap")
shinyjs::enable("hvg_tsneUmap")
shinyjs::enable("scale_tsneUmap")
shinyjs::enable("pca_tsneUmap")
updateCheckboxInput(session, "scale_tsneUmap", value = TRUE)
updateCheckboxInput(session, "pca_tsneUmap", value = TRUE)
}
} else {
shinyjs::enable("reductionMethodUMAPTSNEDimRed")
}
})
observeEvent(input$runDimred_tsneUmap, withConsoleMsgRedirect(
msg = "Please wait while 2D embeddings are being created. See console log for progress.",
{
req(vals$counts)
message(date(), " ... Starting Dimensionality Reduction with: '",
input$dimRedPlotMethod_tsneUmap, "'.")
vals$runDimred$dimRedAssaySelect_tsneUmap <- input$dimRedAssaySelect_tsneUmap
if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% reducedDimNames(vals$counts)) {
embedUseAssay <- NULL
embedUseRedDim <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseAltExp <- NULL
} else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% altExpNames(vals$counts)) {
embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseRedDim <- NULL
embedUseAltExp <- vals$runDimred$dimRedAssaySelect_tsneUmap
} else if (vals$runDimred$dimRedAssaySelect_tsneUmap %in% assayNames(vals$counts)) {
embedUseAssay <- vals$runDimred$dimRedAssaySelect_tsneUmap
embedUseRedDim <- NULL
embedUseAltExp <- NULL
}
if (input$dimRedNameInput_tsneUmap == "" ||
is.null(input$dimRedNameInput_tsneUmap)){
stop("Please enter a reducedDim name!")
}
if (input$dimRedNameInput_tsneUmap %in% names(reducedDims(vals$counts))){
stop("A reducedDim with name '", input$dimRedNameInput_tsneUmap,
"' is already stored in the object. Please specify a ",
"different name for this reducedDim.")
}
dimrednamesave <- gsub(" ", "_", input$dimRedNameInput_tsneUmap)
useFeatureSubset <- input$hvg_tsneUmap
if (input$hvg_tsneUmap == "None") {
useFeatureSubset <- NULL
}
if (input$dimRedPlotMethod_tsneUmap == "rTSNE"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "rTSNE",
logNorm = input$logNorm_tsneUmap,
useFeatureSubset = useFeatureSubset,
center = input$scale_tsneUmap,
scale = input$scale_tsneUmap,
pca = input$pca_tsneUmap,
initialDims = input$dimRedNumberDims_tsneUmap,
theta = input$thetaTSNE,
reducedDimName = dimrednamesave,
perplexity = input$perplexityTSNE,
nIterations = input$iterTSNE,
seed = input$seed__tsneUmap
)
} else if(input$dimRedPlotMethod_tsneUmap == "seuratTSNE"){
if (!is.null(embedUseRedDim)) {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratTSNE",
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
perplexity = input$perplexityTSNE,
seed = input$seed__tsneUmap
)
} else {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratTSNE",
useFeatureSubset = useFeatureSubset,
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
perplexity = input$perplexityTSNE,
useReduction = input$reductionMethodUMAPTSNEDimRed,
seed = input$seed__tsneUmap
)
}
} else if(input$dimRedPlotMethod_tsneUmap == "seuratUMAP"){
if (!is.null(embedUseRedDim)) {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratUMAP",
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
minDist = input$minDistUMAPDimRed,
nNeighbors = input$nNeighboursUMAPDimRed,
spread = input$spreadUMAPDimRed,
seed = input$seed__tsneUmap
)
} else {
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "seuratUMAP",
useFeatureSubset = useFeatureSubset,
reducedDimName = dimrednamesave,
dims = input$dimRedNumberDims_tsneUmap,
minDist = input$minDistUMAPDimRed,
nNeighbors = input$nNeighboursUMAPDimRed,
spread = input$spreadUMAPDimRed,
useReduction = input$reductionMethodUMAPTSNEDimRed,
seed = input$seed__tsneUmap
)
}
} else if(input$dimRedPlotMethod_tsneUmap == "scaterUMAP") {
if (is.na(input$alphaUMAP)) {
stop("Learning rate (alpha) must be a numeric non-empty value!")
}
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scaterUMAP",
logNorm = input$logNorm_tsneUmap,
useFeatureSubset = useFeatureSubset,
scale = input$scale_tsneUmap,
pca = input$pca_tsneUmap,
initialDims = input$dimRedNumberDims_tsneUmap,
reducedDimName = dimrednamesave,
nNeighbors = input$neighborsUMAP,
nIterations = input$iterUMAP,
minDist = input$mindistUMAP,
alpha = input$alphaUMAP,
spread = input$spreadUMAP,
seed = input$seed__tsneUmap
)
} else if(input$dimRedPlotMethod_tsneUmap == "scanpyUMAP"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scanpyUMAP",
reducedDimName = dimrednamesave
)
} else if(input$dimRedPlotMethod_tsneUmap == "scanpyTSNE"){
vals$counts <- runDimReduce(
inSCE = vals$counts,
useAssay = embedUseAssay,
useReducedDim = embedUseRedDim,
useAltExp = embedUseAltExp,
method = "scanpyTSNE",
reducedDimName = dimrednamesave
)
}
updateReddimInputs()
updateAssayInputs()
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-dr", parent = session,
cl = TRUE, cv = TRUE)
message(paste0(date(), " ... Ending Dimensionality Reduction."))
updateSelectizeInput(session, "selectRedDimPlot_tsneUmap",
choices = reducedDimNames(vals$counts),
selected = dimrednamesave,
server = TRUE)
message(paste0(date(), " ... Plotting 2D-Embedding."))
output$plotDimRed_tsneUmap <- renderPlotly({
isolate({
plotly::ggplotly(plotDimRed(
inSCE = vals$counts,
useReduction = dimrednamesave,
xAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_1"),
yAxisLabel = paste0(input$dimRedPlotMethod_tsneUmap,"_2")
))
})
})
}
))
observeEvent(input$updateRedDimPlot_tsneUmap,{
req(vals$counts)
output$plotDimRed_tsneUmap <- renderPlotly({
isolate({
plotly::ggplotly(plotDimRed(
inSCE = vals$counts,
useReduction = input$selectRedDimPlot_tsneUmap,
xAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_1"),
yAxisLabel = paste0(input$selectRedDimPlot_tsneUmap,"_2")
))
})
})
session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
})
observeEvent(input$closeDropDownDimRedEmbedding,{
session$sendCustomMessage("close_dropDownDimRedEmbedding", "")
})
#-----------------------------------------------------------------------------
# Page 3: Clustering ####
#-----------------------------------------------------------------------------
observeEvent(input$clustAlgo, {
if(input$clustAlgo %in% seq(7)){
# Scran SNN
updateTextInput(session, "clustName", value = "scran_snn_cluster")
enable("clustName")
} else if(input$clustAlgo %in% seq(8, 10)){
# K-Means
updateTextInput(session, "clustName", value = "kmeans_cluster")
enable("clustName")
} else if(input$clustAlgo %in% seq(11, 13)){
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Seurat", "_", algo, "_",
"Resolution", input$clustSeuratRes))
disable("clustName")
} else if(input$clustAlgo %in% seq(14, 15)){
algoList <- list('14' = "louvain",
'15' = "leiden")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Scanpy", "_", algo, "_", input$clustSeuratRes))
}
})
observeEvent(input$clustSeuratRes, {
if (input$clustAlgo %in% seq(11, 13)) {
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
updateTextInput(session, "clustName",
value = paste0("Seurat", "_", algo, "_",
"Resolution", input$clustSeuratRes))
disable("clustName")
}
})
clustResults <- reactiveValues(names = NULL)
observeEvent(input$clustRun, withConsoleMsgRedirect(
msg = "Please wait while clustering algorithm is being computed. See console log for progress.",
{
req(vals$counts)
if (input$clustName == "") {
stop("Cluster name should not be empty.")
}
saveClusterName = gsub(" ", "_", input$clustName)
if (input$clustAlgo %in% seq(7)) {
# Scran SNN
if (is.na(input$clustScranSNNK)) {
stop("K must be a numeric non-empty value!")
}
if (is.na(input$clustScranSNNd)) {
stop("Number of components must be a numeric non-empty value!")
}
algoList <- list('1' = "louvain", '2' = "leiden", '3' = "walktrap",
'4' = "infomap", '5' = "fastGreedy",
'6' = "labelProp", '7' = "leadingEigen")
algo <- algoList[[as.character(input$clustAlgo)]]
params = list(inSCE = vals$counts,
clusterName = saveClusterName,
k = input$clustScranSNNK,
weightType = input$clustScranSNNType,
algorithm = algo)
matType <- getTypeByMat(vals$counts, input$clustScranSNNMat)
if (is.null(matType)) {
return()
} else if (length(matType) == 1) {
if (matType == "assay") {
params$useAssay = input$clustScranSNNMat
params$nComp = input$clustScranSNNd
plotReddim <- NULL
} else if (matType == "reducedDim") {
params$useReducedDim = input$clustScranSNNMat
updateSelectInput(session, "clustVisReddim",
selected = input$clustScranSNNMat)
plotReddim <- input$clustScranSNNMat
} else if (matType == "altExp") {
params$useAltExp = input$clustScranSNNMat
params$altExpAssay = input$clustScranSNNMat
params$nComp = input$clustScranSNNd
plotReddim <- NULL
}
} else if (length(matType) == 2 &&
matType[1] == "reducedDim") {
# Using reddims saved in altExp
params$useAltExp = matType[2]
params$altExpRedDim = input$clustScranSNNMat
updateSelectInput(session, "clustVisReddim",
selected = input$clustScranSNNMat)
}
if (algo == 'leiden') {
params$resolution_parameter <- input$clustScranSNNLeidenReso
params$objective_function <- input$clusterScranSNNLeidenObjFunc
}
if (algo == "walktrap") {
params$steps <- input$clustScranSNNWalktrapStep
}
vals$counts <- do.call(runScranSNN, params)
} else if (input$clustAlgo %in% seq(8, 10)) {
# K-Means
if (input$clustKMeansReddim == "") {
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
if (is.na(input$clustKMeansN)) {
stop("Number of clusters/centers must be a numeric non-empty value!")
}
if (is.na(input$clustKMeansNIter)) {
stop("Max number of iterations must be a numeric non-empty value!")
}
if (is.na(input$clustKMeansNStart)) {
stop("Number of random sets must be a numeric non-empty value!")
}
algoList <- list('8' = "Hartigan-Wong",
'9' = "Lloyd", '10' = "MacQueen")
algo <- algoList[[as.character(input$clustAlgo)]]
vals$counts <- runKMeans(inSCE = vals$counts,
useReducedDim = input$clustKMeansReddim,
nCenters = input$clustKMeansN,
nIter = input$clustKMeansNIter,
nStart = input$clustKMeansNStart,
algorithm = algo,
clusterName = saveClusterName)
updateSelectInput(session, "clustVisReddim",
selected = input$clustKMeansReddim)
plotReddim <- input$clustKMeansReddim
} else if (input$clustAlgo %in% seq(11, 13)) {
# Seurat
if(input$clustSeuratReddim == ""){
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
if(is.na(input$clustSeuratDims)){
stop("Number of dimensions must be a numeric non-empty value!")
}
if(is.na(input$clustSeuratRes)){
stop("Resolution must be a numeric non-empty value!")
}
reddim <- reducedDim(vals$counts, input$clustSeuratReddim)
rownames(reddim) <- gsub("_", "-", rownames(reddim))
if ("percentVar" %in% names(attributes(reddim))) {
stdev <- as.numeric(attr(reddim, "percentVar"))
new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
stdev = stdev, key = "PC_")
} else {
new_pca <- CreateDimReducObject(embeddings = reddim, assay = "RNA",
key = "PC_")
}
if (input$clustSeuratDims > ncol(reddim)) {
warning("More dimensions specified in dims than have been computed")
dims <- ncol(reddim)
} else {
dims <- input$clustSeuratDims
}
useAssay <- assayNames(vals$counts)[1]
algoList <- list('11' = "louvain",
'12' = "multilevel", '13' = "SLM")
algo <- algoList[[as.character(input$clustAlgo)]]
vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
useAssay = useAssay,
useReduction = "pca",
externalReduction = new_pca,
dims = dims,
algorithm = algo,
groupSingletons = input$clustSeuratGrpSgltn,
resolution = input$clustSeuratRes)
updateSelectInput(session, "clustVisReddim",
selected = input$clustSeuratReddim)
plotReddim <- input$clustSeuratReddim
}
else if (input$clustAlgo %in% seq(14, 15)){
algoList <- list('14' = "louvain",
'15' = "leiden")
algo <- algoList[[as.character(input$clustAlgo)]]
useAssay <- assayNames(vals$counts)[1] # change this
vals$counts <- runScanpyFindClusters(inSCE = vals$counts,
useAssay = useAssay,
algorithm = algo,
dims = input$clustScanpyDims,
resolution = input$clustScanpyRes,
nNeighbors = input$clustScanpyNeighbors,
niterations = input$clustScanpyIter,
use_weights = input$clustScanpyWeights,
cor_method = input$clustScanpyCorrMethod,
colDataName = input$clustName,
useReduction = input$clustKMeansReddim)
updateSelectInput(session, "clustVisReddim",
selected = input$clustKMeansReddim)
plotReddim <- input$clustKMeansReddim
}
updateColDataNames()
clustResults$names <- c(clustResults$names, saveClusterName)
updateSelectInput(session, "clustVisRes", choices = clustResults$names)
if (!is.null(plotReddim)) {
output$clustVisPlot <- renderPlotly({
isolate({
plotSCEDimReduceColData(inSCE = vals$counts,
colorBy = saveClusterName,
conditionClass = "factor",
reducedDimName = plotReddim,
labelClusters = TRUE,
dim1 = 1, dim2 = 2,
legendTitle = saveClusterName)
})
})
}
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-cl", parent = session,
de = TRUE, fm = TRUE, pa = TRUE, cv = TRUE, tj = TRUE)
}
))
observeEvent(input$closeDropDownClust, {
session$sendCustomMessage("close_dropDownClust", "")
})
observeEvent(input$clustPlot, {
req(vals$counts)
choice <- NULL
if (input$clustVisChoicesType == 1) {
# Use result
if (is.null(input$clustVisRes) ||
input$clustVisRes == "") {
shinyalert::shinyalert("Error!", "Select the clusters to plot",
type = "error")
}
choice <- input$clustVisRes
} else if (input$clustVisChoicesType == 2) {
# Use colData
if (is.null(input$clustVisCol) ||
input$clustVisCol == "") {
shinyalert::shinyalert("Error!", "Select the clusters to plot",
type = "error")
}
choice <- input$clustVisCol
}
if (is.null(input$clustVisReddim) || input$clustVisReddim == "") {
shinyalert::shinyalert("Error!",
"No reduction selected. Select one or run dimension reduction first",
type = "error")
}
if (!is.null(choice) && choice != "" &&
!is.null(input$clustVisReddim) && input$clustVisReddim != "") {
output$clustVisPlot <- renderPlotly({
isolate({
plotSCEDimReduceColData(inSCE = vals$counts,
colorBy = choice,
conditionClass = "factor",
reducedDimName = input$clustVisReddim,
labelClusters = TRUE,
dim1 = 1, dim2 = 2,
legendTitle = choice)
})
})
}
session$sendCustomMessage("close_dropDownClust", "")
})
#-----------------------------------------------------------------------------
# Trajectory Analysis####
#-----------------------------------------------------------------------------
updateTSCANUICollapse <- function() {
if (!is.null(vals$counts)) {
tscanResult <- metadata(vals$counts)$sctk$Traj$TSCAN$Pseudotime
if (is.null(tscanResult)) {
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
} else {
shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::enable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::enable(selector = "div[value='Plot feature expression on trajectory']")
}
} else {
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Path']")
shinyjs::disable(selector = "div[value='Identify Genes Differentially Expressed For Branched Cluster']")
shinyjs::disable(selector = "div[value='Plot feature expression on trajectory']")
}
}
###################################################
### Run STEP 1: TSCAN
###################################################
observeEvent(input$TSCANRun, withConsoleMsgRedirect(
msg = "Please wait while pseudotime is being computed. See console log for progress.",
{
req(vals$counts)
if (input$TSCANReddim == "") {
stop("Must select a reducedDim! If none available, compute one in the Dimensionality Reduction tab.")
}
cluster <- input$TSCANclusterName
if (cluster == "Auto generate clusters") cluster <- NULL
vals$counts <- runTSCAN(inSCE = vals$counts,
useReducedDim = input$TSCANReddim,
cluster = cluster,
seed = handleEmptyInput(input$seed_TSCAN))
output$TSCANPlot <- renderPlot({
isolate({
plotTSCANResults(inSCE = vals$counts,
useReducedDim = input$TSCANReddim)
})
})
results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
terminalNodes <- colnames(results$pseudo)
terminalNodesList <- results$pathIndexList
updatePickerInput(session, "pathIndexx",
choices = terminalNodes,
choicesOpt = list(content=terminalNodesList),
selected = NULL)
clusterNamesList <- sort(unique(colData(vals$counts)$TSCAN_clusters))
updatePickerInput(session, "useClusterForPlotGene",
choices = clusterNamesList,
selected = NULL)
updatePickerInput(session, "plotTSCANDimReduceFeatures_useCluster",
choices = clusterNamesList)
updateSelectInput(session, "TSCANUseCluster",
choices = results$branchClusters)
updateCollapse(session = session, "TSCANUI",
style = list(`Calculate Pseudotime Values` = "success"))
updateTSCANUICollapse()
}
))
#plot results
observeEvent(input$TSCANPlot, {
req(vals$counts)
output$TSCANPlot <- renderPlot({
isolate({
plotTSCANResults(inSCE = vals$counts,
useReducedDim = input$TSCANVisRedDim)
})
})
updateSelectInput(session, "plotTSCANClusterDEG_useReducedDim",
selected = input$TSCANVisRedDim)
updateSelectInput(session, "plotTSCANDimReduceFeatures_useReducedDim",
selected = input$TSCANVisRedDim)
session$sendCustomMessage("close_dropDownTSCAN", "")
})
###################################################
### Run STEP 2: Identify expressive genes
###################################################
observeEvent(input$pathIndexx, {
req(vals$counts)
results <- getTSCANResults(vals$counts, analysisName = "Pseudotime")
choices <- results$pathClusters[[input$pathIndexx]]
updatePickerInput(session, "discardCluster", choices = choices,
selected = NULL,
options = list(
`none-selected-text` = "No cluster discarded"
))
})
observeEvent(input$runTSCANDEG, withConsoleMsgRedirect(
msg = "Please wait while DE genes are being found for path. See console log for progress.",
{
req(vals$counts)
vals$counts <- runTSCANDEG(inSCE = vals$counts,
pathIndex = input$pathIndexx,
useAssay = input$TSCANassayselect,
discardCluster = input$discardCluster)
message(paste0(date(), " ... Expressive Genes Identified"))
message(paste0(date(), " ... Updating heatmap"))
output$heatmapPlot <- renderPlot({
isolate({
plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
pathIndex = input$pathIndexx)
})
})
message(paste0(date(), " ... Updating up-regulated genes"))
output$UpregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$pathIndexx,
direction = "increasing")
})
})
message(paste0(date(), " ... Updating down-regulated genes"))
output$DownregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$pathIndexx,
direction = "decreasing")
})
})
all.results <- getTSCANResults(vals$counts, analysisName = "DEG")
updateSelectInput(session, "tscanDEexpPathIndex",
choices = names(all.results),
selected = input$pathIndexx)
updateCollapse(
session = session, "TSCANUI",
style = list("Identify Genes Differentially Expressed For Path" = "success")
)
callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
de = TRUE, pa = TRUE)
}
))
observeEvent(input$tscanDEPlot, withConsoleMsgRedirect(
msg = "Please wait while TSCAN DE plots are being updated. See console log for progress",
{
req(vals$counts)
if (input$tscanDEFeatureDisplay == "Rownames (Default)") {
featureDisplay <- NULL
} else {
featureDisplay <- input$tscanDEFeatureDisplay
}
message(paste0(date(), " ... Updating heatmap"))
output$heatmapPlot <- renderPlot({
isolate({
plotTSCANPseudotimeHeatmap(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
topN = input$tscanDEHMTopGenes,
featureDisplay = featureDisplay)
})
})
message(paste0(date(), " ... Updating up-regulated genes"))
output$UpregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
direction = "increasing",
topN = input$tscanDERegTopGenes,
featureDisplay = featureDisplay)
})
})
message(paste0(date(), " ... Updating down-regulated genes"))
output$DownregGenesPlot <- renderPlot({
isolate({
plotTSCANPseudotimeGenes(inSCE = vals$counts,
pathIndex = input$tscanDEexpPathIndex,
direction = "decreasing",
topN = input$tscanDERegTopGenes,
featureDisplay = featureDisplay)
})
})
session$sendCustomMessage("close_dropDownTscanDE", "")
}
))
###################################################
### Run STEP 3: Identify DE genes in specific cluster
###################################################
observeEvent(input$findDEGenes, withConsoleMsgRedirect(
msg = "Please wait while DE genes are being found for branched cluster. See console log for progress.",
{
req(vals$counts)
vals$counts <- runTSCANClusterDEAnalysis(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
useAssay = input$TSCANBranchAssaySelect,
fdrThreshold = input$fdrThreshold_TSCAN)
clusterAnalysisNames <- names(getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis"))
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$TSCANUseCluster)
pathChoices <- colnames(results$terminalNodes)
updateSelectInput(session, "plotTSCANClusterDEG_useCluster",
choices = clusterAnalysisNames,
selected = input$TSCANUseCluster)
#plot cluster deg by default
message(paste0(date(), " ... Plotting top DEG expression"))
output$tscanCLusterDEG <- renderPlot({
isolate({
plotTSCANClusterDEG(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
pathIndex = pathChoices[1],
topN = 4,
useReducedDim = input$TSCANVisRedDim)
})
})
#print list of DE genes by default
message(paste0(date(), " ... List of DE genes retrieved"))
df <- as.data.frame(results$DEgenes[[1]])
output$tscanCLusterDEGTable <- DT::renderDataTable({
isolate({
DT::datatable(
df,
options = list(scrollX = TRUE)
)
})
})
#plot cluster pseudo values by default
message(paste0(date(), " ... Plotting pseudotime of branches for cluster"))
output$tscanCLusterPeudo <- renderPlot({
isolate({
plotTSCANClusterPseudo(inSCE = vals$counts,
useCluster = input$TSCANUseCluster,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
})
})
updateCollapse(session = session, "TSCANUI",
style = list("Identify Genes Differentially Expressed For Branched Cluster" = "success"))
callModule(module = nonLinearWorkflow, id = "nlw-Traj", parent = session,
de = TRUE, pa = TRUE)
}
))
# Plot Top DEG expression on cluster
observeEvent(input$plotTSCANClusterDEG_useCluster, {
req(vals$counts)
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$plotTSCANClusterDEG_useCluster)
choices <- colnames(results$terminalNodes)
choicesOpt <- list(content = results$pathIndexList)
updatePickerInput(session, "plotTSCANClusterDEG_pathIndex",
choices = choices, choicesOpt = choicesOpt,
selected = NULL)
})
observeEvent(input$plotTSCANClusterDEG, withConsoleMsgRedirect(
msg = "Please wait while cluster DEG visualization is being updated. See console log for progress.",
{
req(vals$counts)
results <- getTSCANResults(vals$counts,
analysisName = "ClusterDEAnalysis",
pathName = input$plotTSCANClusterDEG_useCluster)
req(results)
if (input$plotTSCANClusterDEG_featureDisplay == "Rownames (Default)") {
featureDisplay <- "rownames"
} else {
featureDisplay <- input$plotTSCANClusterDEG_featureDisplay
}
if (nrow(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]]) == 0) {
shinyalert(text = "No significant feature identified for the selected path.",
type = "warning")
}
message(date(), " ... Updating UMAP with feature expression")
plot <- plotTSCANClusterDEG(inSCE = vals$counts,
useCluster = input$plotTSCANClusterDEG_useCluster,
pathIndex = input$plotTSCANClusterDEG_pathIndex,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim,
topN = handleEmptyInput(input$plotTSCANClusterDEG_topN, type = "numeric"),
featureDisplay = featureDisplay)
output$tscanCLusterDEG <- renderPlot({
isolate({
plot
})
})
message(date(), " ... Updating DEG table")
df <- as.data.frame(results$DEgenes[[input$plotTSCANClusterDEG_pathIndex]])
output$tscanCLusterDEGTable <- DT::renderDataTable({
isolate({
DT::datatable(
df,
options = list(scrollX = TRUE)
)
})
})
message(date(), " ... Updating UMAP with pseudotime")
output$tscanCLusterPeudo <- renderPlot({
isolate({
plotTSCANClusterPseudo(inSCE = vals$counts,
useCluster = input$plotTSCANClusterDEG_useCluster,
useReducedDim = input$plotTSCANClusterDEG_useReducedDim)
})
})
session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
})
)
###################################################
### Run STEP 4: Plot gene of interest
###################################################
observeEvent(input$plotTSCANDimReduceFeatures, withConsoleMsgRedirect(
msg = "Please wait when the expression of selected features are being plotted. See console log for progress.",
{
req(vals$counts)
if (is.null(input$plotTSCANDimReduceFeatures_features)) {
stop("Must select at least one feature.")
}
if (input$plotTSCANDimReduceFeatures_featureDisplay == "Rownames (Default)") {
featureDisplay <- "rownames"
} else {
featureDisplay <- input$plotTSCANDimReduceFeatures_featureDisplay
}
useCluster <- input$plotTSCANDimReduceFeatures_useCluster
output$TscanDimReduceFeatures <- renderPlot({
isolate({
plotTSCANDimReduceFeatures(inSCE = vals$counts,
features = input$plotTSCANDimReduceFeatures_features,
useReducedDim = input$plotTSCANDimReduceFeatures_useReducedDim,
useAssay = input$plotTSCANDimReduceFeatures_useAssay,
useCluster = useCluster,
featureDisplay = featureDisplay)
})
})
updateCollapse(session = session, "TSCANUI",
style = list("Plot feature expression on trajectory" = "success"))
}
))
##############################################################
observeEvent(input$closeDropDownTSCAN,{
session$sendCustomMessage("close_dropDownTSCAN", "")
})
observeEvent(input$closeDropDownTscanDE,{
session$sendCustomMessage("close_dropDownTscanDE", "")
})
observeEvent(input$closeDropDownTscanClusterDEG,{
session$sendCustomMessage("close_dropDownTscanClusterDEG", "")
})
#-----------------------------------------------------------------------------
# Page 3.2: Celda ####
#-----------------------------------------------------------------------------
observeEvent(input$navbar, {
if(!is.null(vals$counts)){
if(input$navbar == "CeldaWorkflow"){
updateSelectInput(session, "celdaassayselect", choices = c(names(assays(vals$counts))))
}
}
})
modsplit <- reactiveVal()
cellsplit <- reactiveVal(NULL)
observeEvent(input$celdamodsplit, withConsoleMsgRedirect(
msg = "Please wait while recursive module split is being computed. See console log for progress.",
{
req(vals$counts)
removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Plot")
removeTab(inputId = "celdaModsplitTabset", target = "Perplexity Difference Plot")
appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Rate of perplexity change",
panel(heading = "RPC Plot",
plotlyOutput(outputId = "plot_modsplit_perpdiff", height = "auto")
)
), select = TRUE)
appendTab(inputId = "celdaModsplitTabset", tabPanel(title = "Perplexity Plot",
panel(heading = "Perplexity Plot",
plotlyOutput(outputId = "plot_modsplit_perp", height = "auto")
)
))
if (input$celdafeatureselect == "None"){
vals$counts <- selectFeatures(vals$counts, minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect)
}else if(input$celdafeatureselect == "runSeuratFindHVG"){
vals$counts <- runSeuratNormalizeData(vals$counts, useAssay = input$celdaassayselect)
vals$counts <- runSeuratFindHVG(vals$counts, useAssay = "seuratNormData",
method = input$celdaseurathvgmethod, hvgNumber = input$celdafeaturenum)
g <- getTopHVG(vals$counts, method = input$celdaseurathvgmethod, n = input$celdafeaturenum)
altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
}else if(input$celdafeatureselect == "Scran_modelGeneVar"){
if (!("ScaterLogNormCounts" %in% names(assays(vals$counts)))){
vals$counts <- scater::logNormCounts(vals$counts, name = "ScaterLogNormCounts",
exprs_values = input$celdaassayselect)
}
vals$counts <- scranModelGeneVar(vals$counts, assayName = "ScaterLogNormCounts")
g <- getTopHVG(vals$counts, method = "modelGeneVar", n = input$celdafeaturenum)
altExp(vals$counts, "featureSubset") <- vals$counts[g, ]
vals$counts <- selectFeatures(vals$counts[g, ], minCount = input$celdarowcountsmin,
minCell = input$celdacolcountsmin, useAssay = input$celdaassayselect, altExpName = "featureSubset")
}
#counts(altExp(vals$counts)) <- as.matrix(counts(altExp(vals$counts)))
updateNumericInput(session, "celdaLselect", min = input$celdaLinit, max = input$celdaLmax, value = input$celdaLinit)
modsplit(recursiveSplitModule(vals$counts, useAssay = input$celdaassayselect, altExpName = "featureSubset", initialL = input$celdaLinit, maxL = input$celdaLmax))
output$plot_modsplit_perpdiff <- renderPlotly({plotRPC(modsplit(), sep = 10)})
output$plot_modsplit_perp <- renderPlotly({plotGridSearchPerplexity(modsplit())})
shinyjs::enable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
shinyjs::enable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
shinyjs::show(selector = ".celda_modsplit_plots")
message(paste0(date(), " ... Module Splitting Complete"))
shinyjs::show(id = "celdaLselect")
shinyjs::show(id = "celdaLbtn")
}
))
observeEvent(input$celdaLbtn, {
vals$counts <- subsetCeldaList(modsplit(), params = list(L = input$celdaLselect))
showNotification("Number of Feature Modules Selected.")
updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Feature Modules" = "success"))
shinyjs::enable(selector = "div[value='Identify Number of Cell Clusters']")
})
output$celdaKplots <- renderUI({
if (!is.null(vals$counts)){
if (!is.null(cellsplit())){
clusterlist <- runParams(cellsplit())$K
plot_output_list <- lapply(runParams(cellsplit())$K, function(i){
plotname <- paste0("Cluster", i)
tabPanel(title = sprintf("Cluster %s", i),
panel(heading = sprintf("Cluster %s", i),
plotlyOutput(plotname)
)
)
})
myTabs <- lapply(clusterlist, tabPanel)
do.call(tabsetPanel, plot_output_list)
}
}
})
observeEvent(input$celdacellsplit, withConsoleMsgRedirect(
msg = "Please wait while recursive split cell is being computed. See console log for progress.",
{
req(vals$counts)
cellsplit(recursiveSplitCell(vals$counts, useAssay = input$celdaassayselect, initialK = input$celdaKinit, maxK = input$celdaKmax,
yInit = celdaModules(vals$counts)))
temp_umap <- celdaUmap(vals$counts)
output$plot_cellsplit_perpdiff <- renderPlotly({plotRPC(cellsplit(), sep = 10)})
output$plot_cellsplit_perp <- renderPlotly({plotGridSearchPerplexity(cellsplit())})
for (i in runParams(cellsplit())$K){
local({
my_i <- i
plotname <- paste0("Cluster", my_i)
celdamod <- subsetCeldaList(cellsplit(), params = list(K = my_i))
output[[plotname]] <- renderPlotly(plotDimReduceCluster(celdamod,
dim1= reducedDim(altExp(temp_umap), "celda_UMAP")[, 1],
dim2 = reducedDim(altExp(temp_umap), "celda_UMAP")[, 2],
labelClusters = TRUE))
})
}
shinyjs::show(selector = ".celda_cellsplit_plots")
message(paste0(date(), " ... Cell Clustering Complete"))
updateNumericInput(session, "celdaKselect", min = input$celdaKinit, max = input$celdaKmax, value = input$celdaKinit)
shinyjs::show(id = "celdaKselect")
shinyjs::show(id = "celdaKbtn")
}
))
observeEvent(input$celdaKbtn, {
vals$counts <- subsetCeldaList(cellsplit(), params = list(K = input$celdaKselect))
showNotification("Number of Cell Clusters Selected.")
updateCollapse(session = session, "CeldaUI", style = list("Identify Number of Cell Clusters" = "success"))
shinyjs::enable(
selector = "div[value='Visualization']")
updateNumericInput(session, "celdamodheatmapnum", min = 1, max = input$celdaLselect, value = 1)
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-celda", parent = session,
de = TRUE, pa = TRUE)
})
output$celdaheatmapplt <- renderPlot({plot(celdaHeatmap(vals$counts))})
output$celdaprobmapplt <- renderPlot({celdaProbabilityMap(vals$counts)})
observeEvent(input$CeldaUmap, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- celdaUmap(vals$counts,
useAssay = input$celdaassayselect,
maxCells = input$celdaUMAPmaxCells,
minClusterSize = input$celdaUMAPminClusterSize,
seed = input$celdaUMAPSeed,
minDist = input$celdaUMAPmindist,
spread = input$celdaUMAPspread,
nNeighbors = input$celdaUMAPnn)
output$celdaumapplot <- renderPlotly({plotDimReduceCluster(vals$counts, reducedDimName = "celda_UMAP", xlab = "UMAP_1",
ylab = "UMAP_2", labelClusters = TRUE)})
message(paste0(date(), " ... UMAP Complete"))
colData(vals$counts)$celda_clusters <- celdaClusters(vals$counts)
updateColDataNames()
shinyjs::enable("CeldaTsne")
}
))
observeEvent(input$CeldaTsne, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- celdaTsne(vals$counts,
useAssay = input$celdaassayselect,
maxCells = input$celdatSNEmaxCells,
minClusterSize = input$celdatSNEminClusterSize,
perplexity = input$celdatSNEPerplexity,
maxIter = input$celdatSNEmaxIter,
seed = input$celdatSNESeed)
output$celdatsneplot <- renderPlotly({
isolate({
plotDimReduceCluster(vals$counts, reducedDimName = "celda_tSNE",
xlab = "tSNE_1", ylab = "tSNE_2",
labelClusters = TRUE)
})
})
message(paste0(date(), " ... tSNE Complete"))
}
))
observeEvent(input$celdamodheatmapbtn,{
output$celdamodheatmapplt <- renderPlot({moduleHeatmap(vals$counts, topCells= input$celdamodheatmaptopcells, featureModule = input$celdamodheatmapnum)})
output$celdamodprobplt <- renderPlot({plotDimReduceModule(vals$counts, modules = input$celdamodheatmapnum, reducedDimName = "celda_UMAP")})
showNotification("Module heatmap complete.")
})
observe({
if(!is.null(vals$counts)){
#If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
shinyjs::enable(
selector = "div[value='Identify Number of Feature Modules']")
}else{
#If no data uploaded in data tab, disabled all tabs and plots.
#Disable tabs
shinyjs::disable(
selector = "div[value='Identify Number of Feature Modules']")
shinyjs::disable(
selector = "div[value='Identify Number of Cell Clusters']")
shinyjs::disable(
selector = "div[value='Visualization']")
#Disable plots inside Modsplit subtab
shinyjs::disable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Plot']")
shinyjs::disable(
selector = ".celda_modsplit_plots a[data-value='Perplexity Diff Plot']")
}
})
#-----------------------------------------------------------------------------
# Page 3.3: Cell Viewer ####
#-----------------------------------------------------------------------------
#-+-+-+-+-+-For Functional Panel collapse##############
shinyjs::onclick("cv_button1", shinyjs::toggle(id = "cv_collapse1",
anim = TRUE), add = TRUE)
shinyjs::onclick("cv_button2", shinyjs::toggle(id = "cv_collapse2",
anim = TRUE), add = TRUE)
shinyjs::onclick("cv_button3", shinyjs::toggle(id = "cv_collapse3",
anim = TRUE), add = TRUE)
shinyjs::addClass(id = "cv_button1", class = "btn-block")
shinyjs::addClass(id = "cv_button2", class = "btn-block")
shinyjs::addClass(id = "cv_button3", class = "btn-block")
colorbrewer_list <- rownames(RColorBrewer::brewer.pal.info)
color_table <- RColorBrewer::brewer.pal.info %>% data.frame()
color_seqdiv <- rownames(color_table[which(color_table$category == "div"
|color_table$category == "seq"),])
#-+-+-+-+-+-For Input Observe##############
observeEvent(input$navbar,{
if (input$navbar == "CellViewer"){
# is there an error or not
if (is.null(vals$counts)){
shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
}else{
gene_list <- rownames(vals$counts)
annotation_list <- names(colData(vals$counts))
annotation_list2 <- list()
for (i in 1:length(annotation_list)){
if(!all.is.numeric(vals$counts[[annotation_list[i]]])){
annotation_list2$Categorical <- c(annotation_list2$Categorical, annotation_list[i])
}else{
annotation_list2$Numeric <- c(annotation_list2$Numeric, annotation_list[i])
}
}
annotation_list <- annotation_list2
rm(annotation_list2)
updateSelectizeInput(session, "GeneSelect_Assays_Xaxis",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Xaxis",
choices = c(annotation_list))
updateSelectizeInput(session, "GeneSelect_Assays_Yaxis",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Yaxis",
choices = c(annotation_list))
updateSelectizeInput(session, "GeneSelect_Assays_Colorby",
choices = c(gene_list), server = TRUE)
updateSelectInput(session, "AnnotationSelect_Colorby",
choices = c(annotation_list))
updateSelectizeInput(session, "adjustgroupby", label = NULL, choices = c("None", annotation_list))
updateSelectizeInput(session,"adjustbrewer", label = "Color Palettes:",
choices = c("RdYlBu",color_seqdiv))
}
}
# if(input$navbar == "Feature Selection & Dimensionality Reduction"){
# gene_list <- rownames(vals$counts)
# updateSelectizeInput(session, "scatterFSGenes",
# choices = c(gene_list),
# server = TRUE)
# }
})
hide_TypeSelect <- reactiveVal("hide")
hide_bins <- reactiveVal()
observeEvent(input$viewertabs, {
if(!is.null(vals$counts)) {
if(!is.null(reducedDims(vals$counts))) {
approach_list <- names(reducedDims(vals$counts))
if (input$viewertabs != "Scatter Plot") {
updateSelectInput(session, "QuickAccess",
choices = c("Custom"))
shinyjs::delay(5,shinyjs::disable("QuickAccess"))
updateSelectInput(session, "TypeSelect_Xaxis",
choices = c("None", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Yaxis",
choices = c("Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Colorby",
selected = "Single Color")
updateSelectInput(session, "adjustgroupby",
selected = "None")
updatePrettyToggle(session, "checkColorbinning",
value = FALSE)
hide_TypeSelect("hide")
shinyjs::delay(5,shinyjs::disable("TypeSelect_Colorby"))
shinyjs::delay(5,shinyjs::disable("adjustgroupby"))
shinyjs::delay(5, shinyjs::disable("adjustlegendtitle"))
shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
} else {
updateSelectInput(session, "QuickAccess",
choices = c("", approach_list, "Custom"))
shinyjs::delay(5,shinyjs::enable("QuickAccess"))
updateSelectInput(session, "TypeSelect_Xaxis",
choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Yaxis",
choices = c("Reduced Dimensions", "Expression Assays", "Cell Annotation"))
updateSelectInput(session, "TypeSelect_Colorby",
selected = "Single Color")
updateSelectInput(session, "adjustgroupby",
selected = "None")
updatePrettyToggle(session, "checkColorbinning",
value = FALSE)
hide_TypeSelect("hide")
shinyjs::delay(5,shinyjs::enable("TypeSelect_Colorby"))
shinyjs::delay(5,shinyjs::enable("adjustgroupby"))
shinyjs::delay(5, shinyjs::enable("adjustlegendtitle"))
if (!is.null(input$adjustgridlines) &
isFALSE(input$adjustgridlines)) {
shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
}
}
if (input$viewertabs != "Bar Plot") {
shinyjs::delay(5, shinyjs::enable("adjustalpha"))
shinyjs::delay(5, shinyjs::enable("adjustsize"))
} else {
shinyjs::delay(5, shinyjs::disable("adjustalpha"))
shinyjs::delay(5, shinyjs::disable("adjustsize"))
}
}
}
})
observeEvent(input$adjustgridlines, {
req(vals$counts)
if (!is.null(input$adjustgridlines)) {
if (isTRUE(input$adjustgridlines)) {
shinyjs::delay(5, shinyjs::disable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::disable("adjustlegendsize"))
shinyjs::delay(5, shinyjs::disable("adjustaxissize"))
shinyjs::delay(5, shinyjs::disable("adjustaxislabelsize"))
} else {
if (input$viewertabs == "Scatter Plot") {
shinyjs::delay(5, shinyjs::enable("adjustlegendtitlesize"))
shinyjs::delay(5, shinyjs::enable("adjustlegendsize"))
}
shinyjs::delay(5, shinyjs::enable("adjustaxissize"))
shinyjs::delay(5, shinyjs::enable("adjustaxislabelsize"))
}
}
})
#-+-+-+-+-+-For Advanced Input Observe##############
###ApproachSelect to DimensionSelect X-Axis
observeEvent(input$ApproachSelect_Xaxis, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Xaxis) & len > 0){
Df <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Xaxis))
xs <- colnames(Df)
updateSelectInput(session, "ColumnSelect_Xaxis", choices = c(xs))
rm(Df)
}
}
})
###ApproachSelect to DimensionSelect Y-Axis
observeEvent(input$ApproachSelect_Yaxis, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Yaxis) & len > 0){
Df2 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Yaxis))
xs2 <- colnames(Df2)
xs2 <- sort(xs2, decreasing = TRUE)
updateSelectInput(session, "ColumnSelect_Yaxis", choices = c(xs2))
rm(Df2)
}
}
})
###ApproachSelect to DimensionSelect Colorby
observeEvent(input$ApproachSelect_Colorby, {
if (!is.null(vals$counts)){
len <- length(SingleCellExperiment::reducedDims(vals$counts))
if (!is.null(input$ApproachSelect_Colorby) & len > 0){
Df3 <- data.frame(SingleCellExperiment::reducedDim(vals$counts,input$ApproachSelect_Colorby))
xs3 <- colnames(Df3)
prefix <- input$ApproachSelect_Colorby
suffix <- seq(1:length(xs3))
columns <- paste(prefix, suffix, sep = "_")
updateSelectInput(session, "ColumnSelect_Colorby", choices = c(columns))
rm(Df3)
}
}
})
#-+-+-+-+-+-Observe Color by###################################################
###Observe Radio Button Select Value Type
# input$AnnotationSelect_Colorby,
observe({
# All inputs to listen for
input$TypeSelect_Colorby
input$AnnotationSelect_Colorby
#Reduced Dimensions
input$ApproachSelect_Colorby
input$ColumnSelect_Colorby
#Expression Assay
input$AdvancedMethodSelect_Colorby
input$GeneSelect_Assays_Colorby
if(input$TypeSelect_Colorby == 'Cell Annotation'){
###If Cell Annotation is not numeric
if(!is.numeric(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("hide")
}else if(is.integer(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])
&length(levels(as.factor(colData(vals$counts)@listData[[input$AnnotationSelect_Colorby]])))<=25){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("show")
}else{
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
}
} else if(input$TypeSelect_Colorby == 'Reduced Dimensions'){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
} else if(input$TypeSelect_Colorby == "Expression Assays"){
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Continuous")
hide_TypeSelect("hide")
} else {
# single color
updateRadioButtons(session, "SelectColorType", "Categorical or Continuous",
choices = c("Categorical", "Continuous"),
selected = "Categorical")
hide_TypeSelect("hide")
}
})
observeEvent(input$SelectColorType,{
if(input$SelectColorType == "Categorical"){
hide_bins("hide")
}else{
hide_bins("show")
}
})
output$hide_typebtns <- renderText({
hide_TypeSelect()
})
outputOptions(output, "hide_typebtns", suspendWhenHidden = FALSE)
output$hide_bins <- renderText({
hide_bins()
})
outputOptions(output, "hide_bins", suspendWhenHidden = FALSE)
numColors <- NULL
colorLabels <- NULL
### Observe input changes that should trigger categorical color generator
observeEvent(c(input$SelectColorType, input$TypeSelect_Colorby, input$AnnotationSelect_Colorby, input$colorTheme), {
if (input$TypeSelect_Colorby == "Single Color") {
shinyjs::hide("categoricalColorConditional")
shinyjs::hide("continuousColorConditional")
}
if (input$SelectColorType == "Categorical" && input$TypeSelect_Colorby != "Single Color") {
if(input$TypeSelect_Colorby == "Cell Annotation") {
req(vals$counts, input$AnnotationSelect_Colorby)
labels = sort(unique(SingleCellExperiment::colData(vals$counts)[, input$AnnotationSelect_Colorby]))
if (length(labels) <=25 ) {
colorLabels <<- labels
numColors <<- length(labels)
defaultColors <- discreteColorPalette(numColors, input$colorTheme)
output$categoricalColorUI <- renderUI({
lapply(1:numColors, function(i){
colourInput(inputId=paste0(i, "_color"), label=labels[i], value=defaultColors[i], showColour="background")
})
})
shinyjs::show("categoricalColorConditional")
shinyjs::hide("continuousColorConditional")
}
}
} else if (input$SelectColorType == "Continuous" && input$TypeSelect_Colorby != "Single Color") {
shinyjs::hide("categoricalColorConditional")
shinyjs::show("continuousColorConditional")
}
})
testvar <- reactive({
event.data <- event_data("plotly_selected")
#curveNumber pointNumber x y
#pointnumbers <- event.data[["pointNumber"]]
})
observeEvent(input$subsetCelda, {
if(!is.null(vals$counts)){
copy <- vals$counts
if(!("featureSubset" %in% altExpNames(copy))){
copy <- selectFeatures(copy)
}
selection <- event_data("plotly_selected")
copy2 <- subsetSCECols(copy, index = testvar()$pointNumber)
clustersog <- celdaClusters(copy)[testvar()$pointNumber]
#copy2 <- subsetSCECols(copy, index = testvar())
maxclust <- max(celdaClusters(copy))
#celdaClusters(copy) <- 0
#celdaClusters(copy) <- celdaClusters(copy2)
#celdaClusters(copy)[testvar()$pointNumber] <- maxclust + 1
#celdaClusters(copy)[colnames(copy) %in% colnames(copy2)] <- clustersog
#celdaClusters(copy)[testvar()] <- 2
saveRDS(copy, "celdasubsettest.RDS")
}
#subset <- testvar()
})
observeEvent(input$subsetCelda2, {
if(!is.null(vals$counts)){
copy <- vals$counts
if(!("featureSubset" %in% altExpNames(copy))){
copy <- selectFeatures(copy)
}
selection <- event_data("plotly_selected")
#copy2 <- subsetSCECols(copy, index = testvar())
#maxclust <- max(celdaClusters(copy))
celdaClusters(copy) <- 1
celdaClusters(copy)[testvar()$pointNumber] <- 2
saveRDS(copy, "celdasubsettest2.RDS")
}
#subset <- testvar()
})
output$testprint <- renderText({as.character(testvar())})
#-+-+-+-+-+-cellviewer prepare step1: choose data. (next steps included)###########################################################
#cellviewer <- eventReactive(input$runCellViewer,{
observeEvent(input$runCellViewer,{
colors <- c()
if (!is.null(numColors) && input$SelectColorType == 'Categorical') {
for (i in 1: numColors) {
colors[i] <- input[[ paste0(i,"_color")]]
}
names(colors) = colorLabels
}
#-+-+-+-+-+-cellviewer prepare3 : prepare Axis Label Name#####################
###Xaxis label name
if (!is.null(input$adjustxlab) &
input$adjustxlab != "") {
xname <- input$adjustxlab
} else {
if (input$QuickAccess != "Custom" &
input$QuickAccess != "") {
# reddim selected
xname <- paste0(input$QuickAccess, 1)
} else if (input$TypeSelect_Xaxis == 'Reduced Dimensions') {
xname <- paste0(input$ApproachSelect_Xaxis, "_",
substr(input$ColumnSelect_Xaxis,
str_length(input$ColumnSelect_Xaxis),
str_length(input$ColumnSelect_Xaxis)))
} else if (input$TypeSelect_Xaxis == 'Expression Assays') {
xname <- input$GeneSelect_Assays_Xaxis
} else if (input$TypeSelect_Xaxis == "Cell Annotation") {
xname <- input$AnnotationSelect_Xaxis
} else {
xname <- ""
}
}
xname <- gsub("-", "_", xname)
###Yaxis label name
if (!is.null(input$adjustylab) &
input$adjustylab != "") {
yname <- input$adjustylab
} else {
if (input$QuickAccess != "Custom" &
input$QuickAccess != "") {
# reddim selected
yname <- paste0(input$QuickAccess, 2)
} else if (input$TypeSelect_Yaxis == 'Reduced Dimensions') {
yname <- paste0(input$ApproachSelect_Yaxis, "_",
substr(input$ColumnSelect_Yaxis,
str_length(input$ColumnSelect_Yaxis),
str_length(input$ColumnSelect_Yaxis)))
} else if (input$TypeSelect_Yaxis == 'Expression Assays') {
yname <- input$GeneSelect_Assays_Yaxis
} else {
yname <- input$AnnotationSelect_Yaxis
}
}
yname <- gsub("-", "_", yname)
###Legend name
if (input$TypeSelect_Colorby != 'Pick a Color') {
if (input$TypeSelect_Colorby == 'Reduced Dimensions' && input$adjustlegendtitle == "") {
legendname <- paste0(input$ApproachSelect_Colorby,"_",substr(input$ColumnSelect_Colorby,
str_length(input$ColumnSelect_Colorby),str_length(input$ColumnSelect_Colorby)))
} else if (input$TypeSelect_Colorby == 'Expression Assays' && input$adjustlegendtitle == "") {
legendname <- input$GeneSelect_Assays_Colorby
} else if (input$adjustlegendtitle == "") {
legendname <- input$AnnotationSelect_Colorby
} else {
legendname <- input$adjustlegendtitle
}
}
legendname <- gsub("-", "_", legendname)
#-+-+-+-+-+-cellviewer prepare4 : choose group by and create plotly function###################
pltVars <- list()
if(input$viewertabs == "Violin/Box Plot" || input$viewertabs == "Bar Plot"){
if(input$TypeSelect_Xaxis == "None"){
pltVars$groupby <- NULL
}else if(input$TypeSelect_Xaxis == "Expression Assays"){
pltVars$groupby <- input$GeneSelect_Assays_Xaxis
}else if(input$TypeSelect_Xaxis == "Cell Annotation"){
pltVars$groupby <- input$AnnotationSelect_Xaxis
}
}else if(input$adjustgroupby != "None"){
pltVars$groupby <- input$adjustgroupby
}else{
pltVars$groupby <- NULL
}
if (input$checkColorbinning == TRUE && input$SelectColorType == "Continuous"){
pltVars$bin <- input$adjustColorbinning
}else{
pltVars$bin <- NULL
}
if (input$SelectColorType == "Categorical"){
pltVars$class <- "factor"
}else{
pltVars$class <- "numeric"
}
if(input$adjustgridlines == TRUE){
pltVars$defTheme <- FALSE
}else{
pltVars$defTheme <- TRUE
}
if(input$viewertabs == "Scatter Plot"){
#### Prepare Custom plotting matrix axis ####
if (input$QuickAccess == "Custom") {
# X Axis
message("CellViewer: Custom plotting mode, making up the axis")
if (input$TypeSelect_Xaxis == "Expression Assays") {
message("X axis: Using expression of ", input$GeneSelect_Assays_Xaxis,
" from ", input$AdvancedMethodSelect_Xaxis)
xvec <- expData(vals$counts, input$AdvancedMethodSelect_Xaxis)[input$GeneSelect_Assays_Xaxis,]
} else if (input$TypeSelect_Xaxis == "Reduced Dimensions") {
message("X axis: Using dimension reduction ", input$ColumnSelect_Xaxis,
" from ", input$ApproachSelect_Xaxis)
xvec <- reducedDim(vals$counts, input$ApproachSelect_Xaxis)[,input$ColumnSelect_Xaxis]
} else if (input$TypeSelect_Xaxis == 'Cell Annotation') {
message("X axis: Using cell annotation ",
input$AnnotationSelect_Xaxis)
xvec <- vals$counts[[input$AnnotationSelect_Xaxis]]
}
# Y Axis
if (input$TypeSelect_Yaxis == "Expression Assays") {
message("Y axis: Using expression of ", input$GeneSelect_Assays_Yaxis,
" from ", input$AdvancedMethodSelect_Yaxis)
yvec <- expData(vals$counts, input$AdvancedMethodSelect_Yaxis)[input$GeneSelect_Assays_Yaxis,]
} else if (input$TypeSelect_Yaxis == "Reduced Dimensions") {
message("Y axis: Using dimension reduction ", input$ColumnSelect_Yaxis,
" from ", input$ApproachSelect_Yaxis)
yvec <- reducedDim(vals$counts, input$ApproachSelect_Yaxis)[,input$ColumnSelect_Yaxis]
} else if (input$TypeSelect_Yaxis == 'Cell Annotation') {
message("Y axis: Using cell annotation ",
input$AnnotationSelect_Yaxis)
yvec <- vals$counts[[input$AnnotationSelect_Yaxis]]
}
# Merge and insert to reducedDim(sce, "Custom")
customMat <- matrix(c(xvec, yvec), nrow = length(xvec))
colnames(customMat) <- c(xname, yname)
rownames(customMat) <- names(xvec)
reducedDim(vals$counts, "Custom") <- customMat
}
if(input$TypeSelect_Colorby == "Single Color"){
a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess,
xlab = xname, ylab = yname, title = input$adjusttitle, groupBy = pltVars$groupby,
transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize,
conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Colorby == "Expression Assays"){
a <- plotSCEDimReduceFeatures(vals$counts, feature = input$GeneSelect_Assays_Colorby,
reducedDimName = input$QuickAccess, useAssay = input$AdvancedMethodSelect_Colorby,
xlab = xname, ylab = yname, legendTitle = legendname, title = input$adjusttitle,
groupBy = pltVars$groupby, bin = pltVars$bin, transparency = input$adjustalpha,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, legendSize = input$adjustlegendsize,
legendTitleSize = input$adjustlegendtitlesize)
} else if (input$TypeSelect_Colorby == "Cell Annotation") {
a <- plotSCEDimReduceColData(vals$counts,reducedDimName = input$QuickAccess,xlab = xname,ylab = yname,
colorBy = input$AnnotationSelect_Colorby,groupBy = pltVars$groupby,legendTitle = legendname,
title = input$adjusttitle,bin = pltVars$bin,transparency = input$adjustalpha,colorScale = colors,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor, dotSize = input$adjustsize,
combinePlot = "none",axisSize = input$adjustaxissize,axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
}else if(input$TypeSelect_Colorby == "Reduced Dimensions"){
a <- plotSCEScatter(vals$counts, reducedDimName = input$QuickAccess, slot = "reducedDims",
annotation = input$ColumnSelect_Colorby, transparency = input$adjustalpha,
colorLow = input$lowColor, colorMid = input$midColor, colorHigh = input$highColor,
groupBy = pltVars$groupby, title = input$adjusttitle, legendTitle = legendname,
xlab = xname, ylab = yname, dotSize = input$adjustsize, bin = pltVars$bin,
combinePlot = "none", axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
legendSize = input$adjustlegendsize, legendTitleSize = input$adjustlegendtitlesize)
}
}else if(input$viewertabs == "Bar Plot"){
if(input$TypeSelect_Yaxis == "Expression Assays"){
a <- plotSCEBarAssayData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
useAssay = input$AdvancedMethodSelect_Yaxis, groupBy = pltVars$groupby,
feature = input$GeneSelect_Assays_Yaxis,
combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Yaxis == "Cell Annotation"){
a <- plotSCEBarColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
coldata = input$AnnotationSelect_Yaxis, groupBy = pltVars$groupby,
combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
defaultTheme = as.logical(pltVars$defTheme))
}
}else if(input$viewertabs == "Violin/Box Plot"){
if(isTRUE(input$vlnboxcheck)){
vln <- TRUE
bx <- FALSE
}else if(isFALSE(input$vlnboxcheck)){
vln <- FALSE
bx <- TRUE
}
if(input$TypeSelect_Yaxis == "Expression Assays"){
a <- plotSCEViolinAssayData(vals$counts, violin = vln, box = bx, xlab = xname, ylab = yname,
useAssay = input$AdvancedMethodSelect_Yaxis, title = input$adjusttitle,
feature = input$GeneSelect_Assays_Yaxis, groupBy = pltVars$groupby,
transparency = input$adjustalpha, dotSize = input$adjustsize, combinePlot = "none",
axisSize = input$adjustaxissize, axisLabelSize = input$adjustaxislabelsize,
defaultTheme = as.logical(pltVars$defTheme))
}else if(input$TypeSelect_Yaxis == "Cell Annotation"){
a <- plotSCEViolinColData(vals$counts, title = input$adjusttitle, xlab = xname, ylab = yname,
coldata = input$AnnotationSelect_Yaxis, violin = vln, box = bx,
groupBy = pltVars$groupby, transparency = input$adjustalpha,
dotSize = input$adjustsize, combinePlot = "none", axisSize = input$adjustaxissize,
axisLabelSize = input$adjustaxislabelsize, defaultTheme = as.logical(pltVars$defTheme))
}
}
if (input$TypeSelect_Colorby == "Single Color"){
a$layers[[1]]$aes_params$colour <- input$Col
}
if (isTRUE(input$adjustgridlines)){
a <- a + ggplot2::theme_bw()
}
a <- plotly::ggplotly(a)
output$scatter <- renderPlotly({
plotly::subplot(plotlist = a, titleX = TRUE, titleY = TRUE)
})
})
#-----------------------------------------------------------------------------
# Page 3.4: Heatmap ####
#-----------------------------------------------------------------------------
hmTemp <- reactiveValues(
sce = NULL,
cellIndex = NULL,
geneIndex = NULL,
colDataName = NULL,
rowDataName = NULL,
colSplitBy = NULL,
rowSplitBy = NULL,
cellTableCol = NULL,
geneTableCol = NULL,
colColorPresets = list(),
rowColorPresets = list()
)
observeEvent(vals$counts, {
if(!is.null(vals$counts)){
hmTemp$sce <- vals$counts
}
})
# Heatmap: Import Analysis ####
observeEvent(input$hmImportRun, {
if(!is.null(vals$counts)){
if(!is.null(input$hmImport) &&
input$hmImport == "Differential Expression"){
if(!is.null(input$hmImpDEG)){
result <- metadata(vals$counts)$diffExp[[input$hmImpDEG]]
useAssay <- result$useAssay
updateSelectInput(session, "hmAssay", selected = useAssay)
method <- result$method
# Cell side
addColData <- data.frame(row.names = colnames(vals$counts))
idx <- rep(NA, ncol(vals$counts))
idx[result$select$ix1] <- result$groupNames[1]
idx[result$select$ix2] <- result$groupNames[2]
hmTemp$cellIndex <- which(!is.na(idx))
conditionColName <- paste(method, input$hmImpDEG, "condition",
sep = '_')
addColData[[conditionColName]] <- factor(idx,
levels = result$groupNames)
colData(hmTemp$sce) <- cbind(colData(hmTemp$sce), addColData)
hmTemp$cellTableCol <- conditionColName
hmTemp$colDataName <- conditionColName
hmTemp$colSplitBy <- conditionColName
hmTemp$colColorPresets[[conditionColName]] <- c('red', 'cyan',
'white')
names(hmTemp$colColorPresets[[conditionColName]]) <-
c(result$groupNames, "NA")
# Gene side
addRowData <- data.frame(row.names = rownames(vals$counts))
deg <- result$result
deg <- deg[stats::complete.cases(deg),]
logFCColName <- paste(method, input$hmImpDEG, "Log2FC",
sep = '_')
FDRColName <- paste(method, input$hmImpDEG, "FDR",
sep = '_')
addRowData[deg$Gene, logFCColName] <- deg$Log2_FC
addRowData[deg$Gene, FDRColName] <- deg$FDR
regColName <- paste(method, input$hmImpDEG, "regulation",
sep = '_')
degUp <- deg[deg$Log2_FC > 0,]
degDown <- deg[deg$Log2_FC < 0,]
addRowData[degUp$Gene, regColName] <- "up"
addRowData[degDown$Gene, regColName] <- "down"
addRowData[[regColName]] <- factor(addRowData[[regColName]],
levels = c('up', 'down'))
rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
hmTemp$geneTableCol <- c(regColName, logFCColName, FDRColName)
hmTemp$geneIndex <- which(rownames(vals$counts) %in% deg$Gene)
hmTemp$rowDataName <- regColName
hmTemp$rowSplitBy <- regColName
hmTemp$rowColorPresets[[regColName]] <- c('red', 'cyan', 'white')
names(hmTemp$rowColorPresets[[regColName]]) <- c('up', 'down', 'NA')
}
} else if (!is.null(input$hmImport) &&
input$hmImport == "Find Marker"){
markerTable <- metadata(vals$counts)$findMarker
if(!is.null(markerTable) &&
dim(markerTable)[1] > 0){
markerTable <- markerTable[stats::complete.cases(markerTable),]
# Cell side
cluster <- colnames(markerTable)[5]
hmTemp$cellIndex <- seq_len(ncol(hmTemp$sce))
hmTemp$colDataName <- cluster
hmTemp$cellTableCol <- cluster
hmTemp$colSplitBy <- cluster
# Gene side
dup.gene <- unique(markerTable$Gene[duplicated(markerTable$Gene)])
for(g in dup.gene){
deg.gix <- markerTable$Gene == g
deg.gtable <- markerTable[deg.gix,]
toKeep <- which.max(deg.gtable$Log2_FC)
toRemove <- which(deg.gix)[-toKeep]
markerTable <- markerTable[-toRemove,]
}
hmTemp$geneIndex <- which(rownames(vals$counts) %in% markerTable$Gene)
addRowData <- data.frame(row.names = rownames(vals$counts))
addRowData[markerTable$Gene, "Marker_for_Cluster"] <- markerTable[,5]
addRowData[markerTable$Gene, "findMarker_Log2FC"] <-
markerTable$Log2_FC
addRowData[markerTable$Gene, "findMarker_FDR"] <- markerTable$FDR
rowData(hmTemp$sce) <- cbind(rowData(hmTemp$sce), addRowData)
hmTemp$geneTableCol <- c("Marker_for_Cluster",
"findMarker_Log2FC",
"findMarker_FDR")
hmTemp$rowDataName <- "Marker_for_Cluster"
hmTemp$rowSplitBy <- "Marker_for_Cluster"
hmTemp$rowColorPresets$Marker_for_Cluster <-
hmAnnAllColors$col[[cluster]]
}
}
}
})
# Heatmap: Subsetting Cells####
output$hmCellColUI <- renderUI({
if(!is.null(vals$counts)){
selectInput(
'hmCellCol',
"Columns to display",
names(colData(hmTemp$sce)), multiple = TRUE, width = '550px',
selected = hmTemp$cellTableCol)
}
})
output$hmCellColTable <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- as.data.frame(colData(hmTemp$sce))
rowNameCol <- data.frame(Row_Names = colnames(vals$counts))
df <- cbind(rowNameCol, df)
rownames(df) <- NULL
DT::datatable(
df,
filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
)
}
}, server = TRUE)
hmCellColTable_proxy <- DT::dataTableProxy("hmCellColTable")
observeEvent(input$hmCellCol, {
colNames <- c('Row_Names', names(colData(hmTemp$sce)))
showIdx <- which(colNames %in% input$hmCellCol)
showIdx <- c(1, showIdx)
DT::showCols(hmCellColTable_proxy, showIdx, reset = TRUE)
})
observeEvent(input$hmCellColTable_state, {
DT::selectRows(hmCellColTable_proxy, hmTemp$cellIndex)
})
observeEvent(input$hmCellColTable_rows_selected, {
hmTemp$cellIndex <- input$hmCellColTable_rows_selected
})
observeEvent(input$hmCellColTable_addAll, {
DT::selectRows(hmCellColTable_proxy,
sort(unique(c(input$hmCellColTable_rows_selected,
input$hmCellColTable_rows_all))))
hmTemp$cellIndex <- sort(unique(c(input$hmCellColTable_rows_selected,
input$hmCellColTable_rows_all)))
})
observeEvent(input$hmCellColTable_clear, {
DT::selectRows(hmCellColTable_proxy, NULL)
hmTemp$cellIndex <- NULL
})
observeEvent(input$hmCellCol, {
hmTemp$cellTableCol <- input$hmCellCol
})
output$hmCellNEnteredUI <- renderUI({
inputList <- str_trim(scan(text = input$hmCellText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
nInput <- length(uniqInput)
if(!is.null(vals$counts) && nInput > 0){
if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmCellTextBy
}
matched <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
by = BY, exactMatch = input$hmCellTextEM,
firstMatch = input$hmCellTextFM)
nMatched <- length(matched)
} else {
nMatched <- 0
}
p(paste0(nInput, " unique input, ", nMatched, "matched."))
})
observeEvent(input$hmCellAddFromText, {
if(!is.null(vals$counts)){
inputList <- str_trim(scan(text = input$hmCellText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
if(length(uniqInput) > 0){
if(!is.null(input$hmCellTextBy) && input$hmCellTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmCellTextBy
}
newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'cell',
by = BY, exactMatch = input$hmCellTextEM,
firstMatch = input$hmCellTextFM)
DT::selectRows(hmCellColTable_proxy,
sort(unique(c(input$hmCellColTable_rows_selected,
newIdx))))
}
}
})
output$hmCellSumUI <- renderUI({
nCell <- length(hmTemp$cellIndex)
if(nCell == 0){
p("No cells selected, going to use them all", style = 'margin-top: 5px;')
} else {
p(paste0("Totally ", nCell, " cells selected."),
style = 'margin-top: 5px;')
}
})
# Heatmap: Subsetting Genes ####
output$hmGeneColUI <- renderUI({
if(!is.null(vals$counts)){
selectInput(
'hmGeneCol',
"Columns to display",
names(rowData(hmTemp$sce)), multiple = TRUE, width = '550px',
selected = hmTemp$geneTableCol)
}
})
output$hmGeneColTable <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- as.data.frame(rowData(hmTemp$sce))
rowNameCol <- data.frame(Row_Names = rownames(vals$counts))
df <- cbind(rowNameCol, df)
rownames(df) <- NULL
DT::datatable(
df,
filter = 'top', options = list(stateSave = TRUE, scrollX = TRUE)
)
}
}, server = TRUE)
hmGeneColTable_proxy <- DT::dataTableProxy("hmGeneColTable")
observeEvent(input$hmGeneCol, {
colNames <- c('Row_Names', names(rowData(hmTemp$sce)))
showIdx <- which(colNames %in% input$hmGeneCol)
showIdx <- c(1, showIdx)
DT::showCols(hmGeneColTable_proxy, showIdx, reset = TRUE)
})
observeEvent(input$hmGeneColTable_state, {
DT::selectRows(hmGeneColTable_proxy, hmTemp$geneIndex)
})
observeEvent(input$hmGeneColTable_rows_selected, {
hmTemp$geneIndex <- input$hmGeneColTable_rows_selected
})
observeEvent(input$hmGeneColTable_addAll, {
DT::selectRows(hmGeneColTable_proxy,
sort(unique(c(input$hmGeneColTable_rows_selected,
input$hmGeneColTable_rows_all))))
hmTemp$geneIndex <- sort(unique(c(input$hmGeneColTable_rows_selected,
input$hmGeneColTable_rows_all)))
})
observeEvent(input$hmGeneColTable_clear, {
DT::selectRows(hmGeneColTable_proxy, NULL)
hmTemp$geneIndex <- NULL
})
observeEvent(input$hmGeneCol, {
hmTemp$geneTableCol <- input$hmGeneCol
})
output$hmGeneNEnteredUI <- renderUI({
inputList <- str_trim(scan(text = input$hmGeneText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
nInput <- length(uniqInput)
if(!is.null(vals$counts) && nInput > 0){
if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmGeneTextBy
}
matched <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
by = BY, exactMatch = input$hmGeneTextEM,
firstMatch = input$hmGeneTextFM)
nMatched <- length(matched)
} else {
nMatched <- 0
}
p(paste0(nInput, " unique input, ", nMatched, "matched."))
})
observeEvent(input$hmGeneAddFromText, {
if(!is.null(vals$counts)){
inputList <- str_trim(scan(text = input$hmGeneText,
sep='\n', what = 'character', quiet = TRUE))
uniqInput <- unique(inputList)
if(length(uniqInput) > 0){
if(!is.null(input$hmGeneTextBy) && input$hmGeneTextBy == 'Row Names'){
BY <- NULL
} else {
BY <- input$hmGeneTextBy
}
newIdx <- retrieveSCEIndex(vals$counts, uniqInput, 'gene',
by = BY, exactMatch = input$hmGeneTextEM,
firstMatch = input$hmGeneTextFM)
DT::selectRows(hmGeneColTable_proxy,
sort(unique(c(input$hmGeneColTable_rows_selected,
newIdx))))
}
}
})
output$hmGeneSumUI <- renderUI({
nGene <- length(hmTemp$geneIndex)
if(nGene == 0){
p("No features selected, going to use them all",
style = 'margin-top: 5px;')
} else {
p(paste0("Totally ", nGene, " features selected."),
style = 'margin-top: 5px;')
}
})
# Heatmap: Annotation color assignment ####
output$hmCellAnnUI <- renderUI({
if(!is.null(vals$counts)){
classes <- names(colData(hmTemp$sce))
selectInput('hmCellAnn', 'Add cell annotation', classes,
multiple = TRUE, selected = hmTemp$colDataName)
}
})
output$hmGeneAnnUI <- renderUI({
if(!is.null(vals$counts)){
classes <- names(rowData(hmTemp$sce))
selectInput('hmGeneAnn', 'Add feature annotation', classes,
multiple = TRUE, selected = hmTemp$rowDataName)
}
})
observeEvent(input$hmCellAnn, {
hmTemp$colDataName <- input$hmCellAnn
})
observeEvent(input$hmGeneAnn, {
hmTemp$rowDataName <- input$hmGeneAnn
})
hmAnnAllColors <- reactiveValues(
col = NULL,
row = NULL
)
generateAnnColAssUI <- function(colname, axis){
if(axis == "row"){
data <- as.vector(rowData(hmTemp$sce)[[colname]])
} else if(axis == 'col'){
data <- as.vector(colData(hmTemp$sce)[[colname]])
}
nUniq <- length(as.vector(unique(data[!is.na(data)])))
if(colname %in% names(hmTemp[[paste0(axis, "ColorPresets")]])){
cats = names(hmTemp[[paste0(axis, "ColorPresets")]][[colname]])
fluidRow(style = "padding-left:20px;",
h4(colname),
lapply(seq_along(cats), function(i) {
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = cats[i],
value = hmTemp[[paste0(axis, "ColorPresets")]][[colname]][[cats[i]]]
)
)
})
)
} else if(nUniq > 12){
if(is.numeric(data)){
fluidRow(style = "padding-left:20px;",
h4(colname),
p(paste0("Numeric annotation with ", nUniq, " unique values detected. Please choose the type of legend.")),
radioButtons(
inputId = paste0('hm', axis, colname, 'type'),
label = NULL,
choices = c('Categorical', 'Continuous'),
inline = TRUE
),
conditionalPanel(
condition = paste0("input.hm", axis, colname, "type == 'Categorical'"),
p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
),
conditionalPanel(
condition = paste0("input.hm", axis, colname, "type == 'Continuous'"),
p("We generate a gradient color legend for continuous annotation value"),
column(
width = 6,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, 'High'),
label = 'High Value'
)
),
column(
width = 6,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, 'Low'),
label = 'Low Value'
)
)
),
)
} else {
fluidRow(style = "padding-left:20px;", h4(colname),
p(paste0("Totally ", nUniq, " unique values in this class of annotation, which is too many to provide manual selection. Coloring will be provided by default."))
)
}
} else if(nUniq >= 1 && nUniq <= 12){
cats <- as.character(unique(data))
fluidRow(style = "padding-left:20px;",
h4(colname),
lapply(seq_along(cats), function(i) {
if(!is.na(cats[i])){
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = cats[i],
value = hmAnnAllColors[[axis]][[colname]][[cats[i]]]
)
)
} else {
column(
width = 3,
colourpicker::colourInput(
inputId = paste0('hm', axis, colname, cats[i]),
label = "NA",
value = #FFFFFF
)
)
}
})
)
} else {
fluidRow(style = "padding-left:20px;",
h4(colname),
p("No effective category found for the class.")
)
}
}
observeEvent(input$hmCellAnn, {
if(!is.null(input$hmCellAnn)){
output$hmCellAnnAssUI <- renderUI({
panel(
lapply(input$hmCellAnn, generateAnnColAssUI, axis = 'col')
)
})
}
})
observeEvent(input$hmGeneAnn, {
if(!is.null(input$hmGeneAnn)){
output$hmGeneAnnAssUI <- renderUI({
panel(
lapply(input$hmGeneAnn, generateAnnColAssUI, axis = 'row')
)
})
}
})
observe({
for (i in names(hmTemp$colColorPresets)){
if (i %in% hmTemp$colDataName){
for (j in names(hmTemp$colColorPresets[[i]])){
if(!is.null(input[[paste0('hmcol', i, j)]])){
hmTemp$colColorPresets[[i]][[j]] <- input[[paste0('hmcol', i, j)]]
}
}
}
}
})
observe({
for (i in names(hmTemp$rowColorPresets)){
if (i %in% hmTemp$rowDataName){
for (j in names(hmTemp$rowColorPresets[[i]])){
if(!is.null(input[[paste0('hmrow', i, j)]])){
hmTemp$rowColorPresets[[i]][[j]] <- input[[paste0('hmrow', i, j)]]
}
}
}
}
})
# Heatmap: Others ####
output$hmColSplitUI <- renderUI({
selectInput(
'hmColSplit',
"Split columns (cell) by (Leave this for not splitting)",
hmTemp$colDataName, multiple = TRUE, selected = hmTemp$colSplitBy
)
})
output$hmRowSplitUI <- renderUI({
selectInput(
'hmRowSplit',
"Split rows (feature) by (Leave this for not splitting)",
hmTemp$rowDataName, multiple = TRUE, selected = hmTemp$rowSplitBy
)
})
observeEvent(input$hmColSplit, {
hmTemp$colSplitBy <- input$hmColSplit
})
observeEvent(input$hmRowSplit, {
hmTemp$rowSplitBy <- input$hmRowSplit
})
output$hmTrimUI <- renderUI({
if(!is.null(vals$counts) && !is.null(input$hmAssay)){
# This might be slow when running with real data
mat <- as.matrix(assay(vals$counts, input$hmAssay))
if(isTRUE(input$hmScale)){
mat <- as.matrix(computeZScore(mat))
}
sliderInput("hmTrim", "Trim", min = floor(min(mat)),
max = ceiling(max(mat)), value = c(-2, 2), step = 0.5)
}
})
# Heatmap: Color Scheme ####
observe({
# Palette preset coding refers:
# https://stackoverflow.com/a/52552008/13676674
vals$hmCSURL <- session$registerDataObj(
name = 'uniquename1',
data = vals$hmCSPresets,
filter = function(data, req) {
query <- parseQueryString(req$QUERY_STRING)
palette <- query$palette
cols <- data[[palette]]
image <- tempfile()
tryCatch({
png(image, width = 75, height = 25, bg = 'transparent')
par(mar = c(0, 0, 0, 0))
barplot(rep(1, length(cols)), col = cols, axes = FALSE)
},finally = dev.off())
shiny:::httpResponse(
200, 'image/png',readBin(image, 'raw', file.info(image)[,'size'])
)
}
)
updateSelectizeInput(
session, 'hmCSPalette', server = TRUE,
choices = names(vals$hmCSPresets),
selected = "RWB",
options = list(
render = I(
sprintf(
"{
option: function(item, escape) {
return '<div><img width=\"75\" height=\"25\" ' +
'src=\"%s&palette=' + escape(item.value) + '\" />' +
escape(item.value) + '</div>';
}
}",
vals$hmCSURL
)
)
)
)
})
observeEvent(input$hmCSPalette, {
if(!input$hmCSPalette == ""){
lowColor <- vals$hmCSPresets[[input$hmCSPalette]][1]
colourpicker::updateColourInput(session, 'hmCSLow', value = lowColor)
}
if(!input$hmCSPalette == ""){
mediumColor <- vals$hmCSPresets[[input$hmCSPalette]][2]
colourpicker::updateColourInput(session, 'hmCSMedium', value = mediumColor)
}
if(!input$hmCSPalette == ""){
highColor <- vals$hmCSPresets[[input$hmCSPalette]][3]
colourpicker::updateColourInput(session, 'hmCSHigh', value = highColor)
}
})
# Heatmap: Final run ####
observeEvent(input$plotHeatmap, {
if (is.null(vals$counts)){
shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
} else {
# Move all plotting process into alert callback, thus auto-re-render can
# be avoided while tuning parameters.
shinyalert(
title = "Confirm",
text = "Large dataset might take time to rerun. Are you sure with the parameters?",
type = "warning",
showCancelButton = TRUE,
confirmButtonText = "Plot",
cancelButtonText = "Check Once More",
callbackR = function(x){
if(isTRUE(x)){
withBusyIndicatorServer("plotHeatmap", {
if(!is.null(hmTemp$colDataName)){
cellAnnColor <- list()
for(i in hmTemp$colDataName){
uniqs <- as.vector(unique(colData(hmTemp$sce)[[i]]))
uniqs[is.na(uniqs)] <- 'NA'
if (i %in% names(hmTemp$colColorPresets)) {
cellAnnColor[[i]] <- hmTemp$colColorPresets[[i]]
} else if (length(uniqs) <= 12) {
cellAnnColor[[i]] <- vector()
for(j in uniqs){
inputId <- paste0('hmcol', i, j)
cellAnnColor[[i]] <- c(cellAnnColor[[i]], input[[inputId]])
}
names(cellAnnColor[[i]]) <- uniqs
} else {
if(is.numeric(colData(hmTemp$sce)[[i]])){
if(input[[paste0('hmcol', i, 'type')]] == 'Continuous'){
cFun <- circlize::colorRamp2(
c(min(colData(hmTemp$sce)[[i]]),
max(colData(hmTemp$sce)[[i]])),
c(input[[paste0('hmcol', i, 'Low')]],
input[[paste0('hmcol', i, 'High')]])
)
cellAnnColor[[i]] <- cFun
} else {
c <- distinctColors(length(uniqs))
names(c) <- uniqs
cellAnnColor[[i]] <- c
}
}
}
}
} else {
cellAnnColor <- NULL
}
if(!is.null(hmTemp$rowDataName)){
geneAnnColor <- list()
for(i in hmTemp$rowDataName){
uniqs <- as.vector(unique(rowData(hmTemp$sce)[[i]]))
if (i %in% names(hmTemp$rowColorPresets)) {
geneAnnColor[[i]] <- hmTemp$rowColorPresets[[i]]
} else if(length(uniqs) <= 12){
geneAnnColor[[i]] <- vector()
for(j in uniqs){
inputId <- paste0('hmrow', i, j)
geneAnnColor[[i]] <- c(geneAnnColor[[i]], input[[inputId]])
}
names(geneAnnColor[[i]]) <- uniqs
} else {
if(is.numeric(rowData(hmTemp$sce)[[i]])){
if(input[[paste0('hmrow', i, 'type')]] == 'Continuous'){
cFun <- circlize::colorRamp2(
c(min(rowData(hmTemp$sce)[[i]]),
max(rowData(hmTemp$sce)[[i]])),
c(input[[paste0('hmrow', i, 'Low')]],
input[[paste0('hmrow', i, 'High')]])
)
geneAnnColor[[i]] <- cFun
} else {
c <- distinctColors(length(uniqs))
names(c) <- uniqs
geneAnnColor[[i]] <- c
}
}
}
}
} else {
geneAnnColor <- NULL
}
hmAddLabel <- list(cell = FALSE, gene = FALSE)
if(!is.null(input$hmAddLabel)){
if("1" %in% input$hmAddLabel){
if(input$hmAddCellLabel == "Default cell IDs"){
hmAddLabel$cell <- TRUE
} else {
hmAddLabel$cell <- input$hmAddCellLabel
}
}
if("2" %in% input$hmAddLabel){
if(input$hmAddGeneLabel == "Default feature IDs"){
hmAddLabel$gene <- TRUE
} else {
hmAddLabel$gene <- input$hmAddGeneLabel
}
}
}
hmShowDendro <- c(FALSE, FALSE)
hmShowDendro[as.numeric(input$hmShowDendro)] <- TRUE
#if(is.null(hmTemp$rowSplitBy)){
# hmRowSplit <- NULL
#} else {
# hmRowSplit <- hmTemp$rowSplitBy
#}
#if(is.null(hmTemp$colSplitBy)){
# hmColSplit <- NULL
#} else {
# hmColSplit <- hmTemp$colSplitBy
#}
cs <- circlize::colorRamp2(
c(input$hmTrim[1], mean(input$hmTrim), input$hmTrim[2]),
c(input$hmCSLow, input$hmCSMedium, input$hmCSHigh)
)
output$Heatmap <- renderPlot({
isolate({
plotSCEHeatmap(
inSCE = hmTemp$sce, useAssay = input$hmAssay, colorScheme = cs,
featureIndex = hmTemp$geneIndex, cellIndex = hmTemp$cellIndex,
rowDataName = hmTemp$rowDataName, colDataName = hmTemp$colDataName,
rowSplitBy = hmTemp$rowSplitBy, colSplitBy = hmTemp$colSplitBy,
rowLabel = hmAddLabel$gene, colLabel = hmAddLabel$cell,
rowDend = hmShowDendro[2], colDend = hmShowDendro[1],
scale = input$hmScale, trim = input$hmTrim,
width = unit(20, 'cm'), height = unit(20, 'cm'),
featureAnnotationColor = geneAnnColor,
cellAnnotationColor = cellAnnColor
)
})
}, height = 800)
})
}
}
)
}
})
# Bubbleplot: Final run ####
observeEvent(input$bpAssay, {
req(vals$counts)
updateSelectInput(session, "bpAssay",
label = "Select Assay")
})
output$bpClusterUI <- renderUI({
req(vals$counts)
selectInput(
'bpCluster',
"",
colnames(colData(vals$counts)), multiple = FALSE, width = '550px')
})
observe({
req(vals$counts)
if (!is.null(metadata(vals$counts)$featureDisplay) && metadata(vals$counts)$featureDisplay %in% names(rowData(vals$counts))) {
featureDisplayValue <- metadata(vals$counts)$featureDisplay
updateSelectizeInput(session, "bpFeatures", choices = rowData(vals$counts)[[featureDisplayValue]], server = TRUE)
}
})
observeEvent(input$plotBubbleplot, {
req(vals$counts)
output$Bubbleplot <- renderPlot({
isolate({
plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures,
displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle,
xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
})
})
})
observeEvent(input$updateBubbleplot, {
req(vals$counts)
output$Bubbleplot <- renderPlot({
isolate({
plotBubble(inSCE=vals$counts, useAssay=input$bpAssay, featureNames=input$bpFeatures,
displayName=input$bpRow, groupNames=input$bpCluster, title=input$bpTitle,
xlab=input$bpX, ylab=input$bpY, colorLow=input$bpLow, colorHigh=input$bpHigh, scale=input$scaleBubble)
})
})
})
# #COG For BubblePlot
# observeEvent(input$closeDropDownBubble, {
# session$sendCustomMessage("close_dropDownBubble", "")
# })
#
# observeEvent(input$bubblePlot, {
# req(vals$counts)
# choice <- NULL
# if (input$bubbleVisChoicesType == 1) {
# # Use result
# if (is.null(input$bubbleVisRes) ||
# input$bubbleVisRes == "") {
# shinyalert::shinyalert("Error!", "Select the clusters to plot",
# type = "error")
# }
# choice <- input$bubbleVisRes
# } else if (input$bubbleVisChoicesType == 2) {
# # Use colData
# if (is.null(input$bubbleVisCol) ||
# input$bubbleVisCol == "") {
# shinyalert::shinyalert("Error!", "Select the clusters to plot",
# type = "error")
# }
# choice <- input$bubbleVisCol
# }
# if (is.null(input$bubbleVisReddim) || input$bubbleVisReddim == "") {
# shinyalert::shinyalert("Error!",
# "No reduction selected. Select one or run dimension reduction first",
# type = "error")
# }
# if (!is.null(choice) && choice != "" &&
# !is.null(input$bubbleVisReddim) && input$bubbleVisReddim != "") {
# output$bubbleVisPlot <- renderPlotly({
# isolate({
# plotSCEDimReduceColData(inSCE = vals$counts,
# colorBy = choice,
# conditionClass = "factor",
# reducedDimName = input$bubbleVisReddim,
# labelClusters = TRUE,
# dim1 = 1, dim2 = 2,
# legendTitle = choice)
# })
# })
# }
# session$sendCustomMessage("close_dropDownBubble", "")
#-----------------------------------------------------------------------------
# Page 4: Batch Correction ####
#-----------------------------------------------------------------------------
observeEvent(input$closeDropDownBC, {
session$sendCustomMessage("close_dropDownBC", "")
})
observeEvent(input$batchCorrMethods, {
if (!is.null(vals$counts) &&
!is.null(input$batchCorrMethods)) {
# What type of assays are required, according their docs
# ComBatSeq - counts
# BBKNN - filtered, normalized, and scaled
# fastMNN - log-expression
# Limma - log-expression
# MNN - log-expression
# scanorama - normalized, log1p
# scMerge - logcounts
# zinbwave - counts
bc.recommended <- NULL
method.log <- c("FastMNN", "Limma", "MNN")
method.scale <- c("BBKNN")
method.raw <- c("ZINBWaVE", "ComBatSeq")
if (is.null(input$batchCorrMethods)) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.log) {
bc.recommended <- c("transformed", "normalized")
} else if (input$batchCorrMethods %in% method.raw) {
bc.recommended <- "raw"
} else if (input$batchCorrMethods %in% method.scale) {
bc.recommended <- "scaled"
}
updateSelectInputTag(session, "batchCorrAssay",
label = "Select Assay to Correct:",
choices = assayNames(vals$counts),
recommended = bc.recommended)
}
})
output$batchCheckResUI <- renderUI({
selectInput("batchCheckCorrName", "Corrected Matrix",
c(names(vals$batchRes)))
})
observeEvent(input$plotBatchCheck, {
if(!is.null(vals$counts) &&
!is.null(input$batchCheckCorrName) &&
input$batchCheckVar != input$batchCheckCond){
withBusyIndicatorServer("plotBatchCheck", {
## Generals
if(input$batchCheckCond == "None"){
shapeBy <- NULL
} else {
shapeBy <- input$batchCheckCond
}
## Original assay PCA
oriAssayPCAName <- paste0(input$batchCheckOrigAssay, "_PCA")
if(!oriAssayPCAName %in% names(reducedDims(vals$counts))){
vals$counts <- scaterPCA(vals$counts,
useAssay = input$batchCheckOrigAssay,
reducedDimName = oriAssayPCAName)
updateReddimInputs()
}
resName <- input$batchCheckCorrName
## Corrected assay/altExp PCA
if (vals$batchRes[[resName]] == 'assay'){
corrAssayPCAName = paste0(resName, "_PCA")
vals$counts <- scaterPCA(vals$counts, useAssay = resName,
reducedDimName = corrAssayPCAName)
updateReddimInputs()
} else if (vals$batchRes[[resName]] == 'altExp'){
ae <- altExp(vals$counts, resName)
corrAltExpPCAName <- paste0(resName, "_PCA")
ae <- scaterPCA(ae, useAssay = resName,
reducedDimName = corrAltExpPCAName)
reducedDim(vals$counts, corrAltExpPCAName) <-
reducedDim(ae, corrAltExpPCAName)
updateReddimInputs()
}
## Update plots
output$batchOriVars <- renderPlot({
isolate({
plotBatchVariance(inSCE = vals$counts,
useAssay = input$batchCheckOrigAssay,
batch = input$batchCheckVar,
condition = shapeBy)
})
})
output$batchOriPCA <- renderPlot({
isolate({
plotSCEDimReduceColData(vals$counts, colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = oriAssayPCAName,
dim1 = 1, dim2 = 2,
title = paste0("Original ",
input$batchCheckOrigAssay,
" PCA"))
})
})
output$batchCorrVars <- renderPlot({
isolate({
if (vals$batchRes[[resName]] == 'reddim'){
plotBatchVariance(inSCE = vals$counts, useReddim = resName,
batch = input$batchCheckVar,
condition = shapeBy)
} else if (vals$batchRes[[resName]] == 'assay'){
plotBatchVariance(inSCE = vals$counts, useAssay = resName,
batch = input$batchCheckVar,
condition = shapeBy)
} else if (vals$batchRes[[resName]] == 'altExp'){
plotBatchVariance(inSCE = vals$counts, useAltExp = resName,
batch = input$batchCheckVar,
condition = shapeBy)
}
})
})
output$batchCorrReddim <- renderPlot({
isolate({
if (vals$batchRes[[resName]] == 'reddim'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = resName,
conditionClass = "character",
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
} else if (vals$batchRes[[resName]] == 'assay'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = corrAssayPCAName,
conditionClass = "character",
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
} else if (vals$batchRes[[resName]] == 'altExp'){
plotSCEDimReduceColData(vals$counts,
colorBy = input$batchCheckVar,
shape = shapeBy,
reducedDimName = corrAltExpPCAName,
dim1 = 1, dim2 = 2,
title = paste0(resName, " corrected"))
}
})
})
})
}
session$sendCustomMessage("close_dropDownBC", "")
})
#observeEvent(input$BBKNNRun, withConsoleMsgRedirect(
# msg = "Please wait while BBKNN method for batch correction is being executed. See console log for progress.",
# {
# req(vals$counts)
# saveassayname <- gsub(" ", "_", input$BBKNNSaveReddim)
# message(date(), " ... Running BBKNN batch correction method")
# vals$counts <- runBBKNN(vals$counts,
# useAssay = input$batchCorrAssay,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$BBKNNNComp)
# message(date(), " ... BBKNN finished")
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# }
#))
observeEvent(input$combatRun, withConsoleMsgRedirect(
msg = "Please wait while CombatSeq method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
#check for zeros
if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
stop("Rows with a sum of zero found. Filter data to continue.")
}
saveassayname <- gsub(" ", "_", input$combatSaveAssay)
if (input$combatKnownCT == "Yes") {
cov <- input$combatCond
} else {
cov <- NULL
}
if (input$combatCTBalance == "Yes") {
useSVA <- FALSE
} else {
useSVA <- TRUE
}
if (input$combatBioCond == "None") {
combatBioCond <- NULL
} else {
combatBioCond <- input$combatBioCond
}
message(date(), " ... Running ComBatSeq batch correction method")
vals$counts <- runComBatSeq(inSCE = vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
covariates = cov,
bioCond = combatBioCond,
useSVA = useSVA,
assayName = saveassayname,
shrink = input$combatShrink,
shrinkDisp = input$combatShrinkDisp,
nGene = input$combatNGene )
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
message(date(), " ... CamBatSeq finished")
}
))
observeEvent(input$FastMNNRun, withConsoleMsgRedirect(
msg = "Please wait while FASTMNN method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$FastMNNSaveReddim)
if(isTRUE(input$FastMNNPcInput)){
fmnnAssay <- input$FastMNNReddim
} else {
fmnnAssay <- input$batchCorrAssay
}
message(date(), " ... Running FastMNN batch correction method")
vals$counts <- runFastMNN(vals$counts,
useAssay = fmnnAssay,
batch = input$batchCorrVar,
reducedDimName = saveassayname,
pcInput = input$FastMNNPcInput
)
message(date(), " ... FastMNN finished")
vals$batchRes[[saveassayname]] <- 'reddim'
updateReddimInputs()
}
))
# TODO: Remember to follow the logging format as other batch correction chunks
# When putting Harmony back
# observeEvent(input$HarmonyRun, {
# if (is.null(vals$counts)){
# shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
# } else {
# withBusyIndicatorServer("HarmonyRun", {
# saveassayname <- gsub(" ", "_", input$HarmonySaveReddim)
# if(isTRUE(input$HarmonyPcInput)){
# useAssay <- input$HarmonyReddim
# } else {
# useAssay <- input$batchCorrAssay
# }
# if(is.na(as.numeric(input$HarmonyTheta))){
# stop("Theta value must be numeric.")
# } else {
# theta <- as.numeric(input$HarmonyTheta)
# }
# vals$counts <- runHarmony(vals$counts, useAssay = useAssay,
# pcInput = input$HarmonyPcInput,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$HarmonyNComp,
# theta = theta, nIter = input$HarmonyNIter)
# shinyalert::shinyalert('Success!', 'Harmony completed.',
# type = 'success')
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# })
# }
# })
observeEvent(input$limmaRun, withConsoleMsgRedirect(
msg = "Please wait while Limma method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
message(date(), " ... Running Limma batch correction method")
vals$counts <- runLimmaBC(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
assayName = saveassayname)
message(date(), " ... Limma batch correction finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
# TODO: Remember to follow the logging format as other batch correction chunks
# When putting LIGER back
#observeEvent(input$ligerRun, {
# if (is.null(vals$counts)){
# shinyalert::shinyalert("Error!", "Upload data first.", type = "error")
# }
# else{
# withBusyIndicatorServer("ligerRun", {
# #check for zeros
# if (any(rowSums(assay(vals$counts, input$batchCorrAssay)) == 0)){
# shinyalert::shinyalert("Error!", "Rows with a sum of zero found. Filter data to continue.", type = "error")
# } else {
# saveassayname <- gsub(" ", "_", input$ligerSaveReddim)
# vals$counts <-
# runLIGER(inSCE = vals$counts,
# useAssay = input$batchCorrAssay,
# batch = input$batchCorrVar,
# reducedDimName = saveassayname,
# nComponents = input$ligerNComp,
# lambda = input$ligerLambda,
# resolution = input$ligerResolution)
# shinyalert::shinyalert('Success!', 'LIGER completed.',
# type = 'success')
# vals$batchRes[[saveassayname]] <- 'reddim'
# updateReddimInputs()
# }
# })
# }
#})
observeEvent(input$MNNRun, withConsoleMsgRedirect(
msg = "Please wait while MNN method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$MNNSaveAssay)
message(date(), " ... Running MNN batch correction method")
vals$counts <- runMNNCorrect(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
k = input$MNNK, sigma = input$MNNSigma,
assayName = saveassayname)
message(date(), " ... MNN finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
observeEvent(input$scnrmRun, withConsoleMsgRedirect(
msg = "Please wait while Scanorama method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$scnrmSaveAssay)
message(date(), " ... Running Scanorama batch correction method")
vals$counts <- runSCANORAMA(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
SIGMA = input$scnrmSIGMA,
ALPHA = input$scnrmALPHA,
KNN = input$scnrmKNN,
assayName = saveassayname)
message(date(), " ... Scanorama finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
observeEvent(input$batchCorrVar, {
req(vals$counts)
req(input$batchCorrVar)
nBatch <- length(unique(colData(vals$counts)[[input$batchCorrVar]]))
output$scMergeNBatch <- renderUI({
span(paste0("Please input ", nBatch, " integer(s), separated by ','."))
})
})
observeEvent(input$scMergeRun, withConsoleMsgRedirect(
msg = "Please wait while scMerge method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$scMergeSaveAssay)
if(input$scMergeSEGOpt == 1){
seg <- NULL
} else if(input$scMergeSEGOpt == 2){
data("SEG")
seg <- SEG[[input$scMergeSEGSpecies]]
} else {
seg <- str_trim(scan(text = input$scMergeSEGCustom,
sep='\n', what = 'character'))
}
if(isTRUE(input$scMergeAutoKmk)){
kmk <- NULL
} else {
kmk <- scan(text = input$scMergeUserKmk, sep=',')
}
message(date(), " ... Running scMerge batch correction method")
vals$counts <- runSCMerge(inSCE = vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
cellType = input$scMergeCT,
seg = seg, kmeansK = kmk,
assayName = saveassayname
)
message(date(), " ... scMerge finished")
vals$batchRes[[saveassayname]] <- 'assay'
updateAssayInputs()
}
))
output$Srt3IntNAnchUI <- renderUI({
if(!is.null(vals$counts)){
ngene <- nrow(vals$counts)
tagList(
numericInput('Srt3IntNAnch', "Number of anchors:",
value = ngene, min = 30, max = ngene, step = 1),
numericInput('Srt3IntKWeight', "kWeight:",
value = 0, min = 0, step = 1),
numericInput('Srt3IntKFilter', "kFilter:",
value = 0, min = 0, step = 1),
numericInput('Srt3IntNDims', "Number of Dimensions:",
value = 2, min = 2, step = 1)
)
}
})
observeEvent(input$Srt3IntRun, withConsoleMsgRedirect(
msg = "Please wait while Seurat3 integration for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$Srt3IntSaveAssay)
message(date(), " ... Running Seurat3 integration method")
vals$counts <- runSeuratIntegration(
inSCE = vals$counts,
batch = input$batchCorrVar,
newAssayName = saveassayname,
kAnchor = input$Srt3IntNAnch,
kWeight = input$Srt3IntKWeight,
kFilter = input$Srt3IntKFilter,
ndims = input$Srt3IntNDims)
message(date(), " ... Seurat3 integration finished")
vals$batchRes[[saveassayname]] <- 'altExp'
}
))
output$zinbwaveNHvgUI <- renderUI({
if(!is.null(vals$counts)){
ngenes <- nrow(vals$counts)
zwdefault <- min(ngenes, 1000)
numericInput('zinbwaveNHVG', 'Number of highly variable genes to use:',
value = zwdefault, max = ngenes)
}
})
output$zinbwaveEpsUI <- renderUI({
if(!is.null(vals$counts)){
ngenes <- nrow(vals$counts)
zwdefault <- min(ngenes, 1000)
numericInput('zinbwaveEps', 'Epsilon value:',
value = zwdefault, max = ngenes)
}
})
observeEvent(input$zinbwaveRun, withConsoleMsgRedirect(
msg = "Please wait while ZINBWaVE method for batch correction is being executed. See console log for progress.",
{
req(vals$counts)
saveassayname <- gsub(" ", "_", input$limmaSaveAssay)
message(date(), " ... Running ZINBWaVE batch correction method")
vals$counts <- runZINBWaVE(vals$counts,
useAssay = input$batchCorrAssay,
batch = input$batchCorrVar,
reducedDimName = saveassayname,
epsilon = input$zinbwaveEps,
nHVG = input$zinbwaveNHVG,
nIter = input$zinbwaveNIter,
nComponents = input$zinbwaveNComp
)
message(date(), " ... ZINBWaVE finished")
vals$batchRes[[saveassayname]] <- 'reddim'
updateReddimInputs()
}
))
#-----------------------------------------------------------------------------
# Page 4.1: Feature Selection ####
#-----------------------------------------------------------------------------
observeEvent(input$hvgMethodFS,{
req(vals$counts)
updateAssayInputs()
})
updateHVGMetricSelection <- function(selected = NULL){
metricList <- lapply(metadata(vals$counts)$sctk$runFeatureSelection,
function(x){x$useAssay})
metricOptions <- names(metricList)
toggleState("hvgMetricSelect", !is.null(metricOptions))
toggleState("hvgNumberSelect", !is.null(metricOptions))
toggleState("hvgSubsetName", !is.null(metricOptions))
toggleState("hvgSubsetRun", !is.null(metricOptions))
toggleState("hvgPlotMethod", !is.null(metricOptions))
toggleState("updatePlotFS", !is.null(metricOptions))
if (!is.null(metricOptions)) {
names(metricOptions) <- paste0(names(metricList), " - ", metricList)
updateSelectInput(session, "hvgPlotMethod", choices = metricOptions,
selected = selected)
updateSelectInput(session, "hvgMetricSelect", choices = metricOptions,
selected = selected)
}
}
updateHVGListSelection <- function(selected = NULL) {
featureSubsets <- names(metadata(vals$counts)$sctk$featureSubsets)
toggleState("hvgPlotSubsetSelect", !is.null(featureSubsets))
toggleState("hvgPlotNLabel", !is.null(featureSubsets))
if (!is.null(featureSubsets)) {
if (is.null(selected)) {
if (!is.null(featureSubsets)) {
selected <- featureSubsets[1]
} else {
selected <- "None"
}
}
updateSelectInput(session, "hvgPlotSubsetSelect",
choices = c("None", featureSubsets),
selected = selected)
updateSelectInput(session, "dimRedHVGSelect",
choices = c("None", featureSubsets),
selected = selected)
updateSelectInput(session, "hvg_tsneUmap",
choices = c("None", featureSubsets),
selected = selected)
}
}
observeEvent(input$findHvgButtonFS, withConsoleMsgRedirect(
msg = "Please wait while variability is being computed. See console log for progress.",
{
req(vals$counts)
vals$counts <- runFeatureSelection(inSCE = vals$counts,
useAssay = input$assaySelectFS_Norm,
method = input$hvgMethodFS)
updateHVGMetricSelection(selected = input$hvgMethodFS)
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgMethodFS,
hvgNumber = 0,
labelsCount = 0)
})
})
}
))
observeEvent(c(input$hvgMetricSelect, input$hvgNumberSelect), {
# Auto made default hvgListName
subsetName <- paste0("HVG_", input$hvgMetricSelect, input$hvgNumberSelect)
updateTextInput(session, "hvgSubsetName", value = subsetName)
})
observeEvent(input$hvgSubsetRun, withConsoleMsgRedirect(
msg = "Please wait feature subset is being created. See console log for progress.",
{
req(vals$counts)
if (input$hvgMetricSelect == "") {
stop("Must calculate variability before selection.")
} else if (input$hvgSubsetName == "") {
stop("Must specify a name for the HVG list.")
} else if (is.na(input$hvgNumberSelect)) {
stop("Must set the number of HVGs for the selection.")
} else {
vals$counts <- setTopHVG(vals$counts,
method = input$hvgMetricSelect,
hvgNumber = input$hvgNumberSelect,
featureSubsetName = input$hvgSubsetName)
updateHVGListSelection(selected = input$hvgSubsetName)
updateHVGMetricSelection(selected = input$hvgMetricSelect)
updateNumericInput(session, "hvgPlotNSelect", value = input$hvgNumberSelect)
featureDisplay <- NULL
if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
featureDisplay <- input$hvgPlotFeatureDisplay
}
# After selection, update visualization part
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgMetricSelect,
useFeatureSubset = input$hvgSubsetName,
labelsCount = 20,
featureDisplay = featureDisplay)
})
})
output$hvgOutputFS <- renderText({
isolate({
getTopHVG(inSCE = vals$counts,
useFeatureSubset = input$hvgSubsetName,
featureDisplay = featureDisplay)
})
})
}
}))
observeEvent(input$updatePlotFS, {
req(vals$counts)
req(metadata(vals$counts)$sctk$runFeatureSelection)
featureDisplay <- NULL
if (input$hvgPlotFeatureDisplay != "Rownames (Default)") {
featureDisplay <- input$hvgPlotFeatureDisplay
}
usefeatureSubset <- input$hvgPlotSubsetSelect
if (input$hvgPlotSubsetSelect == "None") {
usefeatureSubset <- NULL
}
output$plotFS <- renderPlot({
isolate({
plotTopHVG(inSCE = vals$counts,
method = input$hvgPlotMethod,
useFeatureSubset = usefeatureSubset,
labelsCount = input$hvgPlotNLabel,
featureDisplay = featureDisplay)
})
})
output$hvgOutputFS <- renderText({
isolate({
if (!is.null(usefeatureSubset)) {
getTopHVG(inSCE = vals$counts,
useFeatureSubset = usefeatureSubset,
featureDisplay = featureDisplay)
} else {
""
}
})
})
session$sendCustomMessage("close_dropDownFS", "")
})
observeEvent(input$closeDropDownFS, {
session$sendCustomMessage("close_dropDownFS", "")
})
#-----------------------------------------------------------------------------
# Page 5.1: Differential Expression ####
#-----------------------------------------------------------------------------
observeEvent(input$deMethod, {
if (!is.null(vals$counts)) {
if (is.null(input$deMethod)) {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed", "normalized"),
redDims = TRUE)
} else if (input$deMethod == "DESeq2") {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled"),
recommended = c("raw"))
} else {
updateSelectInputTag(session, "deAssay",
tags = c("raw", "transformed", "uncategorized",
"normalized", "scaled", "redDims"),
recommended = c("transformed", "normalized"),
redDims = TRUE)
}
}
})
## DE - Thresholding Vis ####
observeEvent(input$deViewThresh, withConsoleMsgRedirect(
msg = "Please wait while threshold is being plotted. See console log for progress.",
{
req(vals$counts)
req(input$deAssay)
shinyjs::showElement(id= "deThreshpanel")
message(paste0(date(), " ... Plotting thresholding"))
# MAST style sanity check for whether logged or not
x <- expData(vals$counts, input$deAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnThresh <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnThresh <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
suppressMessages({
thres.grob <- plotMASTThresholdGenes(inSCE = vals$counts,
useAssay = input$deAssay,
check_sanity = FALSE,
isLogged = isLogged,
doPlot = FALSE)
})
nSub <- tail(strsplit(thres.grob$childrenOrder, split = '-'),
n = 1)[[1]][3]
plotHeight <- ceiling(as.numeric(nSub) / 4) * 240
output$deThreshPlotDiv <- renderUI({
div(
style = paste0("height: ", plotHeight, "px;"),
plotOutput("deThreshplot"))
})
output$deThreshplot <- renderPlot({
grid.draw(thres.grob)
}, height = plotHeight)
updateActionButton(session, "deViewThresh", "Refresh")
}
))
observeEvent(input$deHideThresh, {
shinyjs::hideElement(id= "deThreshpanel")
updateActionButton(session, "deViewThresh", "View Thresholding")
})
## DE - condition determination method1 ####
output$deC1G1UI <- renderUI({
if(!is.null(vals$counts) &
!input$deC1Class == "None"){
classCol <- colData(vals$counts)[[input$deC1Class]]
classChoices <- sort(as.vector(unique(classCol)))
selectInput(inputId = "deC1G1", label = "Select Condition(s)",
choices = classChoices, multiple = TRUE)
} else {
selectInput(inputId = "deC1G1", label = "Select Condition(s)",
choices = NULL, multiple = TRUE)
}
})
output$deC1G2UI <- renderUI({
if(!is.null(vals$counts) &
!input$deC1Class == "None"){
classCol <- colData(vals$counts)[[input$deC1Class]]
classChoices <- sort(as.vector(unique(classCol)))
selectInput(inputId = "deC1G2", label = "Select Condition(s)",
choices = classChoices, multiple = TRUE)
} else {
selectInput(inputId = "deC1G2", label = "Select Condition(s)",
choices = NULL, multiple = TRUE)
}
})
output$deC1G1CellCheckUI <- renderUI({
if(!is.null(input$deC1G1) &
length(input$deC1G1) > 0){
g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
g1Cells <- colnames(vals$counts)[g1Idx]
g1CellsText <- paste(g1Cells, collapse = "\n")
textAreaInput("deC1G1CellCheck", "Cells selected:", g1CellsText,
height = '100px', placeholder = "Nothing selected")
} else {
textAreaInput("deC1G1CellCheck", "Cells selected:", NULL,
height = '100px', placeholder = "Nothing selected")
}
})
output$deC1G2CellCheckUI <- renderUI({
if(!is.null(input$deC1G2) &
length(input$deC1G2) > 0){
g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
g2Cells <- colnames(vals$counts)[g2Idx]
g2CellsText <- paste(g2Cells, collapse = "\n")
textAreaInput("deC1G2CellCheck", "Cells selected:", g2CellsText,
height = '100px',
placeholder = "Leave unselected for all the others.")
} else {
textAreaInput("deC1G2CellCheck", "Cells selected:", NULL,
height = '100px',
placeholder = "Leave unselected for all the others.")
}
})
output$deC1G1NCell <- renderUI({
if(!is.null(input$deC1G1CellCheck)){
if(!input$deC1G1CellCheck == ""){
cellList <- str_trim(scan(text = input$deC1G1CellCheck,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " cell(s) selected.")
span(msg, style = 'margin-left:10px')
})
output$deC1G2NCell <- renderUI({
if(!is.null(input$deC1G2CellCheck)){
if(!input$deC1G2CellCheck == ""){
cellList <- str_trim(scan(text = input$deC1G2CellCheck,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " cell(s) selected.")
span(msg, style = 'margin-left:10px')
})
## DE - condition determination method2 ####
## condition 1 table operation vvvv
output$deC2G1Table <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- lapply(colData(vals$counts),
function(i){
if(is.character(i) && !length(unique(i)) == length(i)){
return(as.factor(i))
} else if(is.integer(i) &&
!length(unique(i)) == length(i)){
return(as.factor(i))
} else {
return(i)
}
})
df <- data.frame(df, row.names = colnames(vals$counts))
DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
}
}, server = TRUE)
deC2G1Table_proxy <- DT::dataTableProxy("deC2G1Table")
observeEvent(input$deC2G1Col, {
colNames <- names(colData(vals$counts))
showIdx <- which(colNames %in% input$deC2G1Col)
DT::showCols(deC2G1Table_proxy, showIdx, reset = TRUE)
})
observeEvent(input$deC2G1Table_addAll, {
DT::selectRows(deC2G1Table_proxy,
sort(unique(c(input$deC2G1Table_rows_selected,
input$deC2G1Table_rows_all))))
})
observeEvent(input$deC2G1Table_clear, {
DT::selectRows(deC2G1Table_proxy, NULL)
})
output$deC2G1info <- renderUI({
nCell <- length(input$deC2G1Table_rows_selected)
p(paste0("Totally ", nCell, " cells selected for ", input$deG1Name))
})
## condition 1 table operation ^^^^
## condition 2 table operation vvvv
output$deC2G2Table <- DT::renderDataTable({
if(!is.null(vals$counts)){
df <- lapply(colData(vals$counts),
function(i){
if(is.character(i) && !length(unique(i)) == length(i)){
return(as.factor(i))
} else if(is.integer(i) &&
!length(unique(i)) == length(i)){
return(as.factor(i))
} else {
return(i)
}
})
df <- data.frame(df, row.names = colnames(vals$counts))
DT::datatable(df, filter = "top", options = list(scrollX = TRUE))
}
}, server = TRUE)
deC2G2Table_proxy <- DT::dataTableProxy("deC2G2Table")
observeEvent(input$deC2G2Col, {
colNames <- names(colData(vals$counts))
showIdx <- which(colNames %in% input$deC2G2Col)
DT::showCols(deC2G2Table_proxy, showIdx, reset = TRUE)
})
observeEvent(input$deC2G2Table_addAll, {
DT::selectRows(deC2G2Table_proxy,
sort(unique(c(input$deC2G2Table_rows_selected,
input$deC2G2Table_rows_all))))
})
observeEvent(input$deC2G2Table_clear, {
DT::selectRows(deC2G2Table_proxy, NULL)
})
output$deC2G2info <- renderUI({
nCell <- length(input$deC2G2Table_rows_selected)
p(paste0("Totally ", nCell, " cells selected for ", input$deG2Name))
})
## condition 2 table operation ^^^^
## DE - condition determination method3 ####
output$deC3G1NCell <- renderUI({
if(!is.null(input$deC3G1Cell)){
if(!input$deC3G1Cell == ""){
cellList <- str_trim(scan(text = input$deC3G1Cell,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
span(msg, style = 'margin-left:10px')
})
output$deC3G2NCell <- renderUI({
if(!is.null(input$deC3G2Cell)){
if(!input$deC3G2Cell == ""){
cellList <- str_trim(scan(text = input$deC3G2Cell,
sep='\n', what = 'character', quiet = TRUE))
cellList <- unique(cellList)
nCell <- length(which(cellList %in% colnames(vals$counts)))
} else {
nCell <- 0
}
} else {
nCell <- 0
}
msg <- paste0("Totally ", nCell, " valid cell name(s) entered.")
span(msg, style = 'margin-left:10px')
})
# DE run analysis ####
runDEfromShiny <- function(overwrite){
if (input$deAssay %in% assayNames(vals$counts) &
!input$deAssay %in% reducedDimNames(vals$counts)) {
deUseAssay <- input$deAssay
deUseReducedDim <- NULL
} else if (!input$deAssay %in% assayNames(vals$counts) &
input$deAssay %in% reducedDimNames(vals$counts)) {
deUseAssay <- NULL
deUseReducedDim <- input$deAssay
} else {
stop("Error in identifying input matrix")
}
if(input$deCondMethod == 1){
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
class = input$deC1Class,
classGroup1 = input$deC1G1,
classGroup2 = input$deC1G2,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$mastPosOnly,
overwrite = overwrite)
} else if(input$deCondMethod == 2){
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
index1 = input$deC2G1Table_rows_selected,
index2 = input$deC2G2Table_rows_selected,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$dePosOnly,
overwrite = overwrite)
} else {
g1CellList <- str_trim(scan(text = input$deC3G1Cell,
sep='\n', what = 'character', quiet = TRUE))
g1CellList <- sort(unique(g1CellList))
g2CellList <- str_trim(scan(text = input$deC3G2Cell,
sep='\n', what = 'character', quiet = TRUE))
g2CellList <- sort(unique(g2CellList))
vals$counts <- runDEAnalysis(method = input$deMethod,
inSCE = vals$counts,
useAssay = deUseAssay,
useReducedDim = deUseReducedDim,
index1 = g1CellList,
index2 = g2CellList,
groupName1 = input$deG1Name,
groupName2 = input$deG2Name,
analysisName = input$deAnalysisName,
covariates = input$deCovar,
fdrThreshold = input$deFDRThresh,
onlyPos = input$dePosOnly,
overwrite = overwrite)
}
updateDEAnalysisNames(selected = input$deAnalysisName)
colSplitBy <- "condition"
rowSplitBy <- "regulation"
x <- expData(vals$counts, input$deAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
isLogged <- TRUE
} else {
isLogged <- FALSE
updateCheckboxGroupInput(session, "deHMDoLog", selected = TRUE)
}
if (isTRUE(input$deHMShowRowLabel)) {
rowLabel <- TRUE
if (input$deHMrowLabel != "Rownames (Default)" &
is.null(deUseReducedDim)) {
rowLabel <- input$deHMrowLabel
}
} else {
rowLabel <- FALSE
}
message(date(), " ... Updating DE heatmap for analysis: ",
input$deAnalysisName)
output$deHeatmap <- renderPlot({
isolate({
plotDEGHeatmap(inSCE = vals$counts,
useResult = input$deAnalysisName,
onlyPos = input$dePosOnly,
fdrThreshold = input$deFDRThresh,
colSplitBy = colSplitBy,
rowSplitBy = rowSplitBy,
doLog = !isLogged,
rowLabel = rowLabel)
})
})
if (isFALSE(input$deVolcShowLabel)) {
labelTopN <- FALSE
} else if (isTRUE(input$deVolcShowLabel)) {
labelTopN <- input$deVolcTopN
}
message(date(), " ... Updating DE volcano plot for analysis: ",
input$deAnalysisName)
output$deVolcanoPlot <- renderPlot({
isolate({
plotDEGVolcano(inSCE = vals$counts,
useResult = input$deAnalysisName,
log2fcThreshold = input$deVolcLog2FC,
labelTopN = labelTopN,
fdrThreshold = input$deFDRThresh)
})
})
message(date(), " ... Updating DE violin plot for analysis: ",
input$deAnalysisName)
output$deViolinPlot <- renderPlot({
isolate({
plotDEGViolin(inSCE = vals$counts, useResult = input$deAnalysisName,
nrow = input$deVioNRow, ncol = input$deVioNCol,
labelBy = NULL,
check_sanity = FALSE, isLogged = isLogged)
})
})
message(date(), " ... Updating DE regression plot for analysis: ",
input$deAnalysisName)
output$deRegPlot <- renderPlot({
isolate({
plotDEGRegression(inSCE = vals$counts,
useResult = input$deAnalysisName,
nrow = input$deRegNRow,
ncol = input$deRegNCol,
labelBy = NULL,
check_sanity = FALSE,
isLogged = isLogged)
})
})
}
observeEvent(input$runDE, withConsoleMsgRedirect(
msg = "Please wait while DE analysis are being performed. See console log for progress.",
{
req(vals$counts)
if (input$deAnalysisName == "" ||
input$deG1Name == "" ||
input$deG2Name == "") {
stop("The name of the two conditions and the whole analysis have to be specified!")
}
allRes <- names(metadata(vals$counts)$diffExp)
if(input$deAnalysisName %in% allRes){
shinyalert(
"Warning",
"Entered differential experiment analysis name is already there.",
"warning", showCancelButton = TRUE,
confirmButtonText = "Overwrite",
callbackR = function(x){if(isTRUE(x)){runDEfromShiny(x)}})
} else {
runDEfromShiny(FALSE)
}
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-de", parent = session,
pa = TRUE, cv = TRUE)
}
))
updateDEAnalysisNames <- function(selected = NULL) {
deRes <- rev(names(metadata(vals$counts)$diffExp))
if (!is.null(deRes)) {
updateSelectInput(session, "deResSel", choices = deRes,
selected = selected)
updateSelectInput(session, "enrDEGSelect", choices = deRes,
selected = selected)
output$hmImpDEGUI <- renderUI({
selectInput('hmImpDEG', "Import results from analysis:", deRes)
})
} else {
output$hmImpDEGUI <- renderUI({
p("Differential expression analysis not performed yet.")
})
}
}
# DE: Result visualize ####
# Data table
# output$deResult <- DT::renderDataTable({
# if(!is.null(input$deResSel) &&
# !is.null(vals$counts)){
# metadata(vals$counts)$diffExp[[input$deResSel]]$result
# }
# }, filter = 'top')
observeEvent(input$deResSelUpdate, {
if (is.null(input$deResSel) ||
input$deResSel == "") {
shinyjs::disable("deDownload")
} else {
shinyjs::enable("deDownload")
callModule(
module = filterTableServer,
id = "deResult",
dataframe = getDEGTopTable(vals$counts, input$deResSel,
log2fcThreshold = NULL),
defaultFilterColumns = c('Log2_FC', 'FDR'),
defaultFilterOperators = c('>', '<'),
defaultFilterValues = c("1", "0.05"),
initialTopN = 100,
topText = "You can view the differentially epxressed features between the selected groups in the table below. And you can apply customized filters to filter the table accordingly."
)
}
})
output$deDownload <- downloadHandler(
filename = function() {
paste0("deResult_", input$deResSel, ".csv")
},
content = function(file) {
fullTable <- metadata(vals$counts)$diffExp[[input$deResSel]]$result
filteredTable <- fullTable[input$deResult_rows_all,]
filteredTable <- filteredTable[rowSums(is.na(filteredTable)) != ncol(filteredTable), ]
utils::write.csv(filteredTable, file, row.names = FALSE, )
}
)
# Volcano plot
observeEvent(input$closeDropDownDeVolcano, {
session$sendCustomMessage("close_dropDownDeVolcano", "")
})
observeEvent(input$deVolcShowLabel, {
if (isTRUE(input$deVolcShowLabel)) {
enable("deVolcTopN")
enable("deVolcFeatureDisplay")
} else if (isFALSE(input$deVolcShowLabel)) {
disable("deVolcTopN")
disable("deVolcFeatureDisplay")
}
})
observeEvent(list(input$dePlotVolcano, input$deResSelUpdate), {
req(vals$counts)
req(input$deResSel)
if (isFALSE(input$deVolcShowLabel)) {
labelTopN <- FALSE
} else if (isTRUE(input$deVolcShowLabel)) {
labelTopN <- input$deVolcTopN
}
featureDisplay <- NULL
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
if (input$deVolcFeatureDisplay != "Rownames (Default)" &
!is.null(useAssay)) {
featureDisplay <- input$deVolcFeatureDisplay
}
message(date(), " ... Updating DE volcano plot for analysis: ",
input$deResSel)
output$deVolcanoPlot <- renderPlot({
isolate({
plotDEGVolcano(inSCE = vals$counts,
useResult = input$deResSel,
labelTopN = labelTopN,
log2fcThreshold = input$deVolcLog2FC,
fdrThreshold = input$deVolcFDR,
featureDisplay = featureDisplay
)
})
})
session$sendCustomMessage("close_dropDownDeVolcano", "")
})
# Violin plot
output$deVioTotalUI <- renderUI({
topN <- input$deVioNRow * input$deVioNCol
p(as.character(topN))
})
observeEvent(input$closeDropDownDeViolin, {
session$sendCustomMessage("close_dropDownDeViolin", "")
})
observeEvent(list(input$dePlotVio, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == "" &&
!is.null(vals$counts)){
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
labelBy <- NULL
if (input$deVioLabel != "Rownames (Default)" &
!is.null(useAssay)) {
labelBy <- input$deVioLabel
}
# MAST style sanity check for whether logged or not
if (!is.null(useAssay)) {
x <- expData(vals$counts, useAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnViolin <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnViolin <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
} else {
isLogged <- TRUE
}
message(date(), " ... Updating DE violin plot for analysis: ",
input$deResSel)
output$deViolinPlot <- renderPlot({
isolate({
plotDEGViolin(inSCE = vals$counts, useResult = input$deResSel,
#threshP = input$deVioUseThresh,
nrow = input$deVioNRow, ncol = input$deVioNCol,
labelBy = labelBy, check_sanity = FALSE,
isLogged = isLogged)
})
})
session$sendCustomMessage("close_dropDownDeViolin", "")
}
})
# Linear Regression Plot
output$deRegTotalUI <- renderUI({
topN <- input$deRegNRow * input$deRegNCol
p(as.character(topN))
})
observeEvent(input$closeDropDownDeReg, {
session$sendCustomMessage("close_dropDownDeReg", "")
})
observeEvent(list(input$dePlotReg, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == "" &&
!is.null(vals$counts)){
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
labelBy <- NULL
if (input$deVioLabel != "Rownames (Default)" &
!is.null(useAssay)) {
labelBy <- input$deRegLabel
}
# MAST style sanity check for whether logged or not
if (!is.null(useAssay)) {
x <- expData(vals$counts, useAssay)
if (!all(floor(x) == x, na.rm = TRUE) & max(x, na.rm = TRUE) <
100) {
output$deSanityWarnReg <- renderText("")
isLogged <- TRUE
} else {
output$deSanityWarnReg <- renderText("Selected assay seems not logged (MAST style sanity check). Forcing to plot by automatically applying log-transformation. ")
isLogged <- FALSE
}
} else {
isLogged <- TRUE
}
message(date(), " ... Updating DE regression plot for analysis: ",
input$deResSel)
output$deRegPlot <- renderPlot({
isolate({
plotDEGRegression(inSCE = vals$counts,
useResult = input$deResSel,
nrow = input$deRegNRow,
ncol = input$deRegNCol,
labelBy = labelBy,
check_sanity = FALSE,
isLogged = isLogged)
})
})
session$sendCustomMessage("close_dropDownDeReg", "")
}
})
# Heatmap
output$deHMSplitColUI <- renderUI({
otherAvail <- input$deHMcolData
selectInput("deHMSplitCol", "Split columns by", multiple = TRUE,
choices = c('condition', otherAvail),
selected = 'condition')
})
output$deHMSplitRowUI <- renderUI({
otherAvail <- input$deHMrowData
selectInput("deHMSplitRow", "Split rows by", multiple = TRUE,
choices = c('regulation', otherAvail),
selected = 'regulation')
})
observeEvent(input$closeDropDownDeHM, {
session$sendCustomMessage("close_dropDownDeHM", "")
})
observeEvent(list(input$dePlotHM, input$deResSelUpdate), {
if(!is.null(input$deResSel) &&
!input$deResSel == ""){
deHMMinExp1 <- handleEmptyInput(input$deHMMinExp1, "numeric", NULL)
deHMMaxExp2 <- handleEmptyInput(input$deHMMaxExp2, "numeric", NULL)
deHMMinExpPerc1 <- handleEmptyInput(input$deHMMinExpPerc1, "numeric", NULL)
deHMMaxExpPerc2 <- handleEmptyInput(input$deHMMaxExpPerc2, "numeric", NULL)
useAssay <- metadata(vals$counts)$diffExp[[input$deResSel]]$useAssay
rowLabel <- FALSE
if (isTRUE(input$deHMShowRowLabel)) {
rowLabel <- TRUE
if (input$deHMrowLabel != "Rownames (Default)" &
!is.null(useAssay)) {
rowLabel <- input$deHMrowLabel
}
}
message(date(), " ... Updating DE heatmap for analysis: ",
input$deResSel)
output$deHeatmap <- renderPlot({
isolate({
plotDEGHeatmap(inSCE = vals$counts,
useResult = input$deResSel,
doLog = input$deHMDoLog,
onlyPos = input$deHMPosOnly,
log2fcThreshold = input$deHMFC,
fdrThreshold = input$deHMFDR,
rowDataName = input$deHMrowData,
colDataName = input$deHMcolData,
colSplitBy = input$deHMSplitCol,
rowSplitBy = input$deHMSplitRow,
rowLabel = rowLabel)
})
})
session$sendCustomMessage("close_dropDownDeHM", "")
}
})
#-----------------------------------------------------------------------------
# Page 5.2: Find Marker ####
#-----------------------------------------------------------------------------
observeEvent(input$fmMethod, {
if (!is.null(vals$counts)) {
if (is.null(input$fmMethod)) {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
} else if (input$fmMethod == "DESeq2") {
updateSelectInputTag(session, "fmAssay", recommended = c("raw"))
} else {
updateSelectInputTag(session, "fmAssay", recommended = c("transformed", "normalized"))
}
}
})
# findMarker RUN ####
observeEvent(input$runFM, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
fdrThreshold <- handleEmptyInput(input$fmFDR)
vals$counts <- runFindMarker(inSCE = vals$counts,
method = input$fmMethod,
useAssay = input$fmAssay,
cluster = input$fmCluster,
covariates = input$fmCovar,
fdrThreshold = fdrThreshold)
message(date(), " ... Updating find marker heatmap")
updateFMPlot()
callModule(
module = filterTableServer,
id = "filterfmResTable",
dataframe = getFindMarkerTopTable(vals$counts, log2fcThreshold = 0,
minClustExprPerc = 0,
maxCtrlExprPerc = 1,
minMeanExpr = 0, topN = NULL),
defaultFilterColumns = c("Log2_FC", "clusterExprPerc", "ControlExprPerc", "clusterAveExpr"),
defaultFilterOperators = c(">", ">=", "<=", ">="),
defaultFilterValues = c("0", "0", "1", "0"),
initialTopN = 100,
topText = "You can view the markers of each cluster in the table below. And you can apply customized filters to filter the table accordingly."
)
}
))
# findMarker ResultTable ####
# output$fmResTable <- DT::renderDataTable({
# if(!is.null(vals$counts) &&
# 'findMarker' %in% names(metadata(vals$counts))){
# fullTable <- metadata(vals$counts)$findMarker
# fullTable[,5] <- as.factor(fullTable[,5])
# fullTable
# }
# }, filter = "top", options = list(scrollX = TRUE))
observe({
if (!is.null(vals$counts) &&
!is.null(metadata(vals$counts)$findMarker)) {
shinyjs::enable("fmDownload")
} else {
shinyjs::disable("fmDownload")
}
})
output$fmDownload <- downloadHandler(
filename = function() {
paste0("findMarkerResult_", input$fmCluster, ".csv")
},
content = function(file) {
fullTable <- metadata(vals$counts)$findMarker
filteredTable <- fullTable[input$fmResTable_rows_all,]
utils::write.csv(filteredTable, file, row.names = FALSE)
}
)
# findMarker Heatmap ####
observeEvent(input$fmShowHMSetting, {
if (isTRUE(vals$fmHMshowHide)) {
shinyjs::hide("fmHMsettings")
updateActionButton(session, "fmShowHMSetting", label = "Show Settings")
vals$fmHMshowHide <- FALSE
} else {
shinyjs::show("fmHMsettings")
updateActionButton(session, "fmShowHMSetting", label = "Hide Settings")
vals$fmHMshowHide <- TRUE
}
})
observeEvent(input$fmUseTopN, {
if (!isTRUE(input$fmUseTopN)) {
shinyjs::disable("fmTopN")
} else {
shinyjs::enable("fmTopN")
}
})
observeEvent(input$closeDropDownFM, {
session$sendCustomMessage("close_dropDownFM", "")
})
observeEvent(input$plotFM, {
updateFMPlot()
session$sendCustomMessage("close_dropDownFM", "")
})
updateFMPlot <- function() {
if(!is.null(vals$counts) &&
'findMarker' %in% names(metadata(vals$counts))){
withBusyIndicatorServer("plotFM", {
message(paste0(date(), " ... Updating marker heatmap"))
if (isTRUE(input$fmUseTopN)
&& is.na(input$fmTopN)) {
stop("Top N marker must be a numeric non-empty value")
}
if (is.na(input$fmHMFC)) {
stop("Log2FC must be a numeric non-empty value!")
}
if (is.na(input$fmHMFDR)) {
stop("FDR must be a numeric non-empty value!")
}
if (!isTRUE(input$fmUseTopN)) {
topN <- NULL
} else {
topN <- input$fmTopN
}
if (input$fmHMFeatureDisplay != "Rownames (Default)") {
rowLabel <- input$fmHMFeatureDisplay
} else {
rowLabel <- TRUE
}
# Take value before rendering plot, so that the plot doesn't auto
# re-render while we tweak the parameter
output$fmHeatmap <- renderPlot({
isolate({
plotFindMarkerHeatmap(inSCE = vals$counts,
orderBy = input$fmHMOrder,
log2fcThreshold = input$fmHMFC,
topN = topN,
fdrThreshold = input$fmHMFDR,
decreasing = input$fmHMdec,
rowDataName = input$fmHMrowData,
colDataName = input$fmHMcolData,
minClustExprPerc = input$fmHMMinClustExprPerc,
maxCtrlExprPerc = input$fmHMMaxCtrlExprPerc,
minMeanExpr = input$fmHMMinMeanExpr,
rowLabel = rowLabel)
})
})
})
}
}
#-----------------------------------------------------------------------------
# Page 6: Pathway Activity Analysis
#-----------------------------------------------------------------------------
observeEvent(input$pathwayImportGS, {
showTab(inputId = "navbar",
target = "Import Gene Sets",
select = TRUE,
session = session)
})
#colData for grouping the data (optional for user)
observeEvent(input$pathway, {
if(!is.null(vals$counts)){
updateSelectInput(session, "pathwayPlotVar", choices = colnames(colData(vals$counts)))
}
})
#select geneset collection name for pathway analysis
#output$selectPathwayGeneLists <- renderUI({
# if (!is.null(vals$counts)){
# if (!is.null(metadata(vals$counts)$sctk$genesets)) {
# newGSchoices <- sctkListGeneSetCollections(vals$counts)
# selectizeInput("PathwayGeneLists", "Select Geneset Collection(s):",
# choices = newGSchoices, multiple = FALSE)
# }
# } else {
# HTML("<h5><span style='color:red'>Must import geneset data first!</span></h5></br>")
# }
#})
#Run algorithm
observeEvent(input$pathwayRun, withConsoleMsgRedirect(
msg = "Please wait while pathway analysis are being performed. See console log for progress.",
{
req(vals$counts)
if (input$PathwayGeneLists == "Import geneset before using") {
stop("Must import geneset first.", type = "error")
}
if (input$pathway == "VAM") {
vals$counts <- runVAM(inSCE = vals$counts,
useAssay = input$vamAssay,
geneSetCollectionName = input$PathwayGeneLists,
center = input$vamCenterParameter,
gamma = input$vamGammaParameter)
scoreSelect <- paste0("VAM_", input$PathwayGeneLists, "_CDF")
} else if (input$pathway == "GSVA") {
vals$counts <- runGSVA(inSCE = vals$counts,
useAssay = input$vamAssay,
geneSetCollectionName = input$PathwayGeneLists)
scoreSelect <- paste0("GSVA_", input$PathwayGeneLists, "_Scores")
}
updateAssayInputs()
updateReddimInputs()
availPathwayRes <- getPathwayResultNames(vals$counts)
firstGS <- colnames(reducedDim(vals$counts, scoreSelect))[1]
updateSelectizeInput(session, "pathwayRedDimNames",
choices = availPathwayRes, selected = scoreSelect)
updateSelectizeInput(session, "pathwayPlotGS",
choices = colnames(reducedDim(vals$counts, scoreSelect)),
selected = firstGS)
#plot results with default values intitially
output$pathwayPlot <- renderPlot({
isolate({
plotPathway(inSCE = vals$counts,
resultName = scoreSelect,
geneset = firstGS,
groupBy = input$pathwayPlotVar,
boxplot = input$pathwayPlotBoxplot,
violin = input$pathwayPlotViolinplot,
dots = input$pathwayPlotDots,
summary = input$pathwayPlotSummary)
})
})
}
))
observeEvent(input$pathwayRedDimNames, {
if (!is.null(vals$counts)) {
updateSelectizeInput(session, "pathwayPlotGS",
choices = colnames(reducedDim(vals$counts, input$pathwayRedDimNames)))
}
})
#plot results
observeEvent(input$pathwayPlot, {
output$pathwayPlot <- renderPlot({
isolate({
plotPathway(inSCE = vals$counts,
resultName = input$pathwayRedDimNames,
geneset = input$pathwayPlotGS,
groupBy = input$pathwayPlotVar,
boxplot = input$pathwayPlotBoxplot,
violin = input$pathwayPlotViolinplot,
dots = input$pathwayPlotDots,
summary = input$pathwayPlotSummary)
})
})
session$sendCustomMessage("close_dropDownPathway", "")
})
observeEvent(input$closeDropDownPathway,{
session$sendCustomMessage("close_dropDownPathway", "")
})
#disable downloadPathway button if the pathway data doesn't exist
#isVamResult <- reactive(is.null(vals$vamResults))
#isGsvaResult <- reactive(is.null(vals$gsvaResults))
#observe({
# if (isVamResult() && isGsvaResult()) {
# shinyjs::disable("downloadPathway")
# } else {
# shinyjs::enable("downloadPathway")
# }
#})
#download pathway results
#output$downloadPathway <- downloadHandler(
# filename = function() {
# paste("Pathway_results-", Sys.Date(), ".csv", sep = "")
# },
# content = function(file) {
# if(input$pathway == "VAM"){
# utils::write.csv(vals$vamResults, file)
# }
# else if (input$pathway == "GSVA"){
# utils::write.csv(vals$gsvaResults, file)
# }
# }
#)
#-----------------------------------------------------------------------------
# Page 6.2 : Enrichment Analysis - EnrichR ####
#-----------------------------------------------------------------------------
enrichRfile <- reactive(read.csv(input$enrFile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote,
row.names = 1))
updateEnrichRAnalysisNames <- function(selected = NULL) {
if (is.null(selected)) {
selected <- input$enrAnalysisNameSel
}
allNames <- names(metadata(vals$counts)$sctk$runEnrichR)
updateSelectInput(session, "enrAnalysisNameSel",
label = "Select analysis name:",
choices = allNames,
selected = selected)
}
update_enrDEG <- reactive({
list(input$enrDEGSelect, input$enrDEGUpOnly, input$enrDEGlog2fc,
input$enrDEGFDR, input$enrDEGminMean1, input$enrDEGmaxMean2,
input$enrDEGminPerc1, input$enrDEGmaxPerc2)
})
observeEvent(ignoreInit = TRUE, update_enrDEG(), {
req(vals$counts)
req(input$enrDEGSelect)
degSelect <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
labelBy = NULL, onlyPos = input$enrDEGUpOnly,
log2fcThreshold = input$enrDEGlog2fc,
fdrThreshold = input$enrDEGFDR,
minGroup1MeanExp = input$enrDEGminMean1,
maxGroup2MeanExp = input$enrDEGmaxMean2,
minGroup1ExprPerc = input$enrDEGminPerc1,
maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
nGene <- length(degSelect)
output$enrDEGText <- renderUI(p(paste0("Selected ", nGene, " DEGs. Listed below.")))
output$enrDEGRes <- renderText({
isolate({
degSelect
})
})
})
#count_db <- reactive(length(dbs()))
observeEvent (input$enrichRun, withConsoleMsgRedirect(
msg = "Please wait while EnrichR is running. See console log for progress.",
{
req(vals$counts)
if (!internetConnection) {
stop("Internet connection failed.")
}
if (input$enrAnalysisNameSet == "" |
is.null(input$enrAnalysisNameSet)) {
stop("The analysis name has to be specified")
}
by <- "rownames"
if (input$geneListChoice == "deg") {
genes <- getDEGTopTable(vals$counts, useResult = input$enrDEGSelect,
labelBy = NULL, onlyPos = input$enrDEGUpOnly,
log2fcThreshold = input$enrDEGlog2fc,
fdrThreshold = input$enrDEGFDR,
minGroup1MeanExp = input$enrDEGminMean1,
maxGroup2MeanExp = input$enrDEGmaxMean2,
minGroup1ExprPerc = input$enrDEGminPerc1,
maxGroup2ExprPerc = input$enrDEGmaxPerc2)$Gene
} else if (input$geneListChoice == "selectGenes"){
genes <- input$enrichGenes
} else if (input$geneListChoice == "geneFile"){
req(input$enrFile)
genes <- rownames(enrichRfile())
message(date(), " ... Reading from file. The first three features are:")
message(date(), " ", paste(genes[seq(3)], collapse = ", "))
by <- input$enrFileBy
}
message(date(), " ... Performing GSEA with enrichR")
vals$counts <- runEnrichR(inSCE = vals$counts,
features = genes,
analysisName = input$enrAnalysisNameSet,
db = input$enrichDb,
by = by,
featureName = input$enrFeatureName)
updateEnrichRAnalysisNames(selected = input$enrAnalysisNameSet)
}
))
enrChangeDBShow <- reactive({
list(input$enrAnalysisNameSel,
input$enrichRun)
})
observeEvent(enrChangeDBShow(), {
req(input$enrAnalysisNameSel)
dbs <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
updateSelectizeInput(session, "enrDbShow", choices = dbs,
selected = input$enrDbShow)
})
enrResultSel <- reactive({
list(input$enrAnalysisNameSel,
input$enrDbShow,
input$enrichRun)
})
#create datatables
observeEvent(enrResultSel(), {
req(vals$counts)
req(input$enrAnalysisNameSel)
res <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$result
dbToShow <- input$enrDbShow
if (is.null(dbToShow)) {
dbToShow <- getEnrichRResult(vals$counts, input$enrAnalysisNameSel)$param$db
}
res <- res[which(res[, 1] %in% dbToShow), ]
vals$enrichRes <- res
#tableToShow <- res[, c(1:10)] %>%
# mutate(Database_selected =
# paste0("<a href='", res[, 11],
# "' target='_blank'>",
# res[, 1], "</a>"))
tableToShow <- res
output$enrDataTable <- DT::renderDataTable({
DT::datatable({
tableToShow
},
escape = FALSE,
options = list(scrollX = TRUE, pageLength = 20),
rownames = FALSE)
})
})
#disable the downloadEnrichR button if the result doesn't exist
isResult <- reactive(is.null(vals$enrichRes))
observe({
if (isResult()) {
shinyjs::disable("downloadEnrichR")
} else {
shinyjs::enable("downloadEnrichR")
}
})
output$downloadEnrichR <- downloadHandler(
filename = function() {
paste0("SCTK_enrichR_results_", input$enrAnalysisNameSel, "_",
Sys.Date(), ".csv")
},
content = function(file) {
utils::write.csv(vals$enrichRes, file, row.names = FALSE)
},
contentType = "text/csv"
)
#-----------------------------------------------------------------------------
# Page 7: Subsampling
#-----------------------------------------------------------------------------
#Run subsampling analysis
observeEvent(input$runSubsampleDepth, withConsoleMsgRedirect(
msg = "Please wait while subsampler is being computed. See console log for progress.",
{
req(vals$counts)
if(is.na(input$minCount)){
stop("Minimum readcount must be a non-empty numeric value!")
}
if(is.na(input$minCells)){
stop("Minimum number of cells must be a non-empty numeric value!")
}
if(is.na(input$iterations)){
stop("Number of bootstrap iterations must be a non-empty numeric value!")
}
vals$subDepth <- downSampleDepth(originalData = vals$counts,
useAssay = input$depthAssay,
minCount = input$minCount,
minCells = input$minCells,
maxDepth = 10 ^ input$maxDepth,
realLabels = input$selectReadDepthCondition,
depthResolution = input$depthResolution,
iterations = input$iterations)
output$depthDone <- renderPlot({
plot(apply(vals$subDepth[, , 1], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of detected genes",
main = "Number of dected genes by sequencing depth")
lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 1], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$minEffectDone <- renderPlot({
plot(apply(vals$subDepth[, , 2], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Average significant effect size",
ylim = c(0, 2))
lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 2], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$sigNumDone <- renderPlot({
plot(apply(vals$subDepth[, , 3], 2, median)~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution),
lwd = 4, xlab = "log10(Total read counts)", ylab = "Number of significantly DiffEx genes")
lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.25)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subDepth[, , 3], 2, function(x){quantile(x, 0.75)})~
seq(from = 0, to = input$maxDepth, length.out = input$depthResolution), lty = 2, lwd = 3)
})
}
))
observeEvent(input$runSubsampleCells, withConsoleMsgRedirect(
msg = "Please wait while resampler is being computed. See console log for progress.",
{
req(vals$counts)
if (is.na(input$minCellNum) ||
is.na(input$maxCellNum) ||
is.na(input$iterations) ||
is.na(input$totalReads) ||
is.na(input$minCount) ||
is.na(input$minCells) ||
is.na(input$depthResolution)) {
stop("One or more parameter values are empty!")
}
if (input$useReadCount) {
vals$subCells <- downSampleCells(originalData = vals$counts,
useAssay = input$cellsAssay,
realLabels = input$selectCellNumCondition,
totalReads = sum(SummarizedExperiment::assay(vals$counts, input$cellsAssay)),
minCellnum = input$minCellNum,
maxCellnum = input$maxCellNum,
minCountDetec = input$minCount,
minCellsDetec = input$minCells,
depthResolution = input$depthResolution,
iterations = input$iterations)
} else {
vals$subCells <- downSampleCells(originalData = vals$counts,
useAssay = input$cellsAssay,
realLabels = input$selectCellNumCondition,
totalReads = input$totalReads,
minCellnum = input$minCellNum,
maxCellnum = input$maxCellNum,
minCountDetec = input$minCount,
minCellsDetec = input$minCells,
depthResolution = input$depthResolution,
iterations = input$iterations)
}
output$cellsDone <- renderPlot({
plot(apply(vals$subCells[, , 1], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of virtual cells", ylab = "Number of detected genes",
main = "Number of dected genes by cell number")
lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 1], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$minEffectCells <- renderPlot({
plot(apply(vals$subCells[, , 2], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of virtual cells", ylab = "Average significant effect size",
ylim = c(0, 2))
lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 2], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
output$sigNumCells <- renderPlot({
plot(apply(vals$subCells[, , 3], 2, median)~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution),
lwd = 4, xlab = "Number of vitual cells", ylab = "Number of significantly DiffEx genes")
lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.25)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
lines(apply(vals$subCells[, , 3], 2, function(x){quantile(x, 0.75)})~
seq(from = input$minCellNum, to = input$maxCellNum, length.out = input$depthResolution), lty = 2, lwd = 3)
})
}
))
#Run differential power analysis
observeEvent(input$runSnapshot, withConsoleMsgRedirect(
msg = "Please wait while resampling snapshot is being computed. See console log for progress.",
{
req(vals$counts)
if (is.na(input$numCellsSnap) ||
is.na(input$numReadsSnap) ||
is.na(input$iterationsSnap)) {
stop("One or more parameter values are empty!")
}
vals$snapshot <- iterateSimulations(originalData = vals$counts,
useAssay = input$snapshotAssay,
realLabels = input$selectSnapshotCondition,
totalReads = input$numReadsSnap,
cells = input$numCellsSnap,
iterations = input$iterationsSnap)
vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapshotCondition])
output$Snaplot <- renderPlot({
plot(apply(vals$snapshot, 1, function(x){sum(x <= 0.05) / length(x)}) ~ vals$effectSizes,
xlab = "Cohen's d effect size", ylab = "Detection power", lwd = 4, main = "Power to detect diffex by effect size")
})
}
))
#-----------------------------------------------------------------------------
# Page 8: Seurat Workflow
#-----------------------------------------------------------------------------
#Perform normalization
observeEvent(input$normalize_button, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Normalizing Data"))
vals$counts <- runSeuratNormalizeData(inSCE = vals$counts,
useAssay = input$seuratSelectNormalizationAssay,
normAssayName = "seuratNormData",
normalizationMethod = input$normalization_method,
scaleFactor = as.numeric(input$scale_factor))
metadata(vals$counts)$sctk$seuratUseAssay <- input$seuratSelectNormalizationAssay
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts)
updateCollapse(session = session, "SeuratUI", style = list("Normalize Data" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Highly Variable Genes']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
updateAssayInputs()
message(paste0(date(), " ... Normalization Complete"))
}))
# #Perform scaling
# observeEvent(input$scale_button, withConsoleMsgRedirect ({
# #shows the notification spinner and console log
# .loadOpen ("Please wait while data is being scaled. See console log for progress.")
#
# req(vals$counts)
# message(paste0(date(), " ... Scaling Data"))
# vals$counts <- runSeuratScaleData(inSCE = vals$counts,
# useAssay = "seuratNormData",
# scaledAssayName = "seuratScaledData",
# #model = input$model.use,
# scale = input$do.scale,
# center = input$do.center,
# scaleMax = input$scale.max)
#
# vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE)
#
# updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "success"))
# shinyjs::enable(selector = "div[value='Dimensionality Reduction']")
# S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
# shinyjs::hide(
# selector = "div[value='Downstream Analysis']")
# message(paste0(date(), " ... Scaling Complete"))
#
# .loadClose() #close the notification spinner and console log
# }))
#Find HVG
observeEvent(input$find_hvg_button, withConsoleMsgRedirect(
msg = "Please wait while high variable genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding High Variable Genes"))
if(input$hvg_method == "vst" || packageVersion(pkg = "SeuratObject") >= 5.0){
vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
useAssay = metadata(vals$counts)$sctk$seuratUseAssay,
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features))
}
else{
vals$counts <- runSeuratFindHVG(inSCE = vals$counts,
useAssay = "seuratNormData",
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features))
}
vals$counts <- setTopHVG(inSCE = vals$counts,
method = input$hvg_method,
hvgNumber = as.numeric(input$hvg_no_features),
featureSubsetName = "featureSubset")
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, varFeatures = FALSE)
message(paste0(date(), " ... Plotting HVG"))
output$plot_hvg <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratHVG(vals$counts, input$hvg_no_features_view))
})
})
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... Finding HVG Complete"))
}
))
#Display highly variable genes
output$hvg_output <- renderText({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$obj)) {
if(packageVersion(pkg = "SeuratObject") >= 5.0){
if (length(vals$counts@metadata$seurat$obj$RNA$"var.features") > 0) {
isolate({
singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
})
}
}
else{
if (length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) > 0) {
isolate({
singleCellTK:::.seuratGetVariableFeatures(vals$counts, input$hvg_no_features_view)
})
}
}
}
})
#Run PCA
observeEvent(input$run_pca_button, withConsoleMsgRedirect(
msg = "Please wait while PCA is being computed. See console log for progress.",
{
req(vals$counts)
#remove tabs if not generated
removeTab(inputId = "seuratPCAPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Elbow Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "JackStraw Plot")
removeTab(inputId = "seuratPCAPlotTabset", target = "Heatmap Plot")
message(paste0(date(), " ... Running PCA"))
# For the commented line below:
# `useFeatureSubset`, in any functions that use it, goes to util function
# `.parseUseFeatureSubset()` which does a check for rownames(inSCE). Thus
# incompatible with Seurat's "_"-to-"-" change. But in `runSeuratPCA/ICA`,
# we automatically detect seurat HVG from the object when `useFeatureSubset
# = NULL`, so no need to specify this now.
vals$counts <- runSeuratPCA(inSCE = vals$counts,
useAssay = "seuratNormData",
reducedDimName = "seuratPCA",
#useFeatureSubset = getSeuratVariableFeatures(vals$counts),
nPCs = input$pca_no_components,
seed = input$seed_PCA,
scale = TRUE)
vals$counts@metadata$seurat$count_pc <- dim(convertSCEToSeurat(vals$counts)[["pca"]])[2]
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca")
)
), select = TRUE)
message(paste0(date(), " ... Plotting PCA"))
output$plot_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "pca",
showLegend = FALSE))
})
})
if (input$pca_compute_elbow) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Elbow Plot",
panel(
heading = "Elbow Plot",
plotlyOutput(outputId = "plot_elbow_pca")
)
))
message(paste0(date(), " ... Generating Elbow Plot"))
updateNumericInput(session = session, inputId = "pca_significant_pc_counter", value = singleCellTK:::.computeSignificantPC(vals$counts))
output$plot_elbow_pca <- renderPlotly({
isolate({
plotSeuratElbow(inSCE = vals$counts,
significantPC = singleCellTK:::.computeSignificantPC(vals$counts))
})
})
output$pca_significant_pc_output <- renderText({
isolate({
paste("<p>Number of significant components suggested by ElbowPlot: <span style='color:red'>", singleCellTK:::.computeSignificantPC(vals$counts)," </span> </p> <hr>")
})
})
}
if (input$pca_compute_jackstraw) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "JackStraw Plot",
panel(heading = "JackStraw Plot",
plotlyOutput(outputId = "plot_jackstraw_pca")
)
))
message(paste0(date(), " ... Generating JackStraw Plot"))
vals$counts <- runSeuratJackStraw(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally
dims = input$pca_no_components)
output$plot_jackstraw_pca <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratJackStraw(inSCE = vals$counts,
dims = input$pca_no_components))
})
})
}
if (input$pca_compute_heatmap) {
appendTab(inputId = "seuratPCAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(4, dropdown(
fluidRow(
column(12,
fluidRow(actionBttn(inputId = "closeDropDownSeuratHM", label = NULL, style = "simple", color = "danger", icon = icon("times"), size = "xs"), align = "right"),
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_pca", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_pca", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionBttn(
inputId = "plot_heatmap_pca_button",
label = "Update",
style = "bordered",
color = "primary",
size = "sm"
)
)
),
inputId = "dropDownSeuratHM",
icon = icon("cog"),
status = "primary",
circle = FALSE,
inline = TRUE
)),
column(7, fluidRow(h6("Heatmaps of the top features correlated with each component"), align="center"))
)
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_pca"), options = list(maxWidth = 700))
)
)
))
message(paste0(date(), " ... Generating Heatmaps"))
vals$counts@metadata$seurat$heatmap_pca <- runSeuratHeatmap(inSCE = vals$counts,
useAssay = "seuratNormData",
useReduction = "pca",
dims = input$pca_no_components,
nfeatures = input$pca_compute_heatmap_nfeatures,
combine = FALSE,
fast = FALSE)
output$plot_heatmap_pca <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
dims = input$pca_no_components,
ncol = 2,
labels = c("PC1", "PC2", "PC3", "PC4"))
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_pca", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_pc, "PC"))
}
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
#Enable/Disable PCA plot panels not selected for computation (ElbowPlot, JackStraw or Heatmap)
shinyjs::enable(
selector = ".seurat_pca_plots a[data-value='PCA Plot']")
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='Elbow Plot']",
condition = input$pca_compute_elbow)
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='JackStraw Plot']",
condition = input$pca_compute_jackstraw)
shinyjs::toggleState(
selector = ".seurat_pca_plots a[data-value='Heatmap Plot']",
condition = input$pca_compute_heatmap)
shinyjs::enable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".seurat_pca_plots")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... PCA Complete"))
}))
observeEvent(input$closeDropDownSeuratHM,{
session$sendCustomMessage("close_dropDownSeuratHM", "")
})
#Run ICA
observeEvent(input$run_ica_button, withConsoleMsgRedirect(
msg = "Please wait while ICA is being computed. See console log for progress.",
{
req(vals$counts)
#remove tabs if not generated
removeTab(inputId = "seuratICAPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratICAPlotTabset", target = "Heatmap Plot")
message(paste0(date(), " ... Running ICA"))
vals$counts <- runSeuratICA(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally with scale = TRUE
nics = input$ica_no_components,
seed = input$seed_ICA,
scale = TRUE)
vals$counts@metadata$seurat$count_ic <- dim(convertSCEToSeurat(vals$counts)[["ica"]])[2]
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE)
appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "ICA Plot",
panel(heading = "ICA Plot",
plotlyOutput(outputId = "plot_ica")
)
), select = TRUE)
message(paste0(date(), " ... Plotting ICA"))
output$plot_ica <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "ica",
showLegend = FALSE))
})
})
if (input$ica_compute_heatmap) {
appendTab(inputId = "seuratICAPlotTabset", tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
panel(heading = "Plot Options",
fluidRow(
column(6,
pickerInput(inputId = "picker_dimheatmap_components_ica", label = "Select principal components to plot:", choices = c(), options = list(`actions-box` = TRUE, size = 10, `selected-text-format` = "count > 3"), multiple = TRUE)
),
column(6,
sliderInput(inputId = "slider_dimheatmap_ica", label = "Number of columns for the plot: ", min = 1, max = 4, value = 2)
)
),
actionButton(inputId = "plot_heatmap_ica_button", "Plot")
),
panel(heading = "Plot",
shinyjqui::jqui_resizable(plotOutput(outputId = "plot_heatmap_ica"), options = list(maxWidth = 700))
)
)
))
message(paste0(date(), " ... Generating Heatmaps"))
vals$counts@metadata$seurat$heatmap_ica <- runSeuratHeatmap(inSCE = vals$counts,
useAssay = "seuratNormData", # scales internally
useReduction = "ica",
dims = input$ica_no_components,
nfeatures = input$ica_compute_heatmap_nfeatures,
combine = FALSE,
fast = FALSE)
output$plot_heatmap_ica <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
dims = input$ica_no_components,
ncol = 2,
labels = c("IC1", "IC2", "IC3", "IC4"))
})
})
updatePickerInput(session = session, inputId = "picker_dimheatmap_components_ica", choices = singleCellTK:::.getComponentNames(vals$counts@metadata$seurat$count_ic, "IC"))
}
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "success"))
#Enable/Disable ICA plot panels not selected for computation (Heatmap)
shinyjs::enable(
selector = ".seurat_ica_plots a[data-value='ICA Plot']")
shinyjs::toggleState(
selector = ".seurat_ica_plots a[data-value='Heatmap Plot']",
condition = input$ica_compute_heatmap)
shinyjs::enable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".seurat_ica_plots")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... ICA Complete"))
}))
#Find clusters
observeEvent(input$find_clusters_button, withConsoleMsgRedirect(
msg = "Please wait while clusters are being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToCluster = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_clustering_method]]
}
else
pathToCluster = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_clustering_method]]
if(!is.null(pathToCluster)){
#Remove plot tabs if generated before
removeTab(inputId = "seuratClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "seuratClusteringPlotTabset", target = "UMAP Plot")
message(paste0(date(), " ... Clustering Dataset"))
vals$counts <- runSeuratFindClusters(inSCE = vals$counts,
useReduction = input$reduction_clustering_method,
dims = input$pca_significant_pc_counter,
algorithm = input$algorithm.use,
groupSingletons = input$group.singletons,
resolution = input$resolution_clustering)
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "success"))
message(paste0(date(), " ... Finding Clusters Complete"))
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToUMAP = vals$counts@metadata$seurat$obj$"reductions"[["umap"]]
}
else
pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[["umap"]]
if(!is.null(pathToUMAP)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotlyOutput(outputId = "plot_umap_clustering")
)
), select = TRUE)
message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
output$plot_umap_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "umap",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='UMAP Plot']",
condition = !is.null(pathToUMAP))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToTSNE = vals$counts@metadata$seurat$obj$"reductions"[["tsne"]]
}
else
pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[["tsne"]]
if(!is.null(pathToTSNE)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotlyOutput(outputId = "plot_tsne_clustering")
)
), select = TRUE)
message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
output$plot_tsne_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "tsne",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='tSNE Plot']",
condition = !is.null(pathToTSNE))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToPCA = vals$counts@metadata$seurat$obj$"reductions"[["pca"]]
}
else
pathToPCA = vals$counts@metadata$seurat$obj@"reductions"[["pca"]]
if(!is.null(pathToPCA)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotlyOutput(outputId = "plot_pca_clustering")
)
)
)
message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
output$plot_pca_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "pca",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='PCA Plot']",
condition = !is.null(pathToPCA))
}
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToICA = vals$counts@metadata$seurat$obj$"reductions"[["ica"]]
}
else
pathToICA = vals$counts@metadata$seurat$obj@"reductions"[["ica"]]
if(!is.null(pathToICA)){
appendTab(inputId = "seuratClusteringPlotTabset", tabPanel(title = "ICA Plot",
panel(heading = "ICA Plot",
plotlyOutput(outputId = "plot_ica_clustering")
)
))
message(paste0(date(), " ... Re-generating ICA Plot with Cluster Labels"))
output$plot_ica_clustering <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "ica",
showLegend = TRUE))
})
})
shinyjs::toggleState(
selector = ".seurat_clustering_plots a[data-value='ICA Plot']",
condition = !is.null(pathToICA))
}
shinyjs::show(selector = ".seurat_clustering_plots")
#enable find marker selection
shinyjs::enable(
selector = "#SeuratUI > div[value='Find Markers']")
#update colData names
updateColDataNames()
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
#populate updated colData items for findMarkers tab
updateSelectInput(session = session,
inputId = "seuratFindMarkerSelectPhenotype",
choices = colnames(colData(vals$counts)),
selected = paste0(
"Seurat_",
input$algorithm.use,
"_Resolution",
input$resolution_clustering))
#populate reducDim objects from seuratObject for findMarkers tab
updateSelectInput(session = session,
inputId = "seuratFindMarkerReductionMethod",
choices = Seurat::Reductions(convertSCEToSeurat(vals$counts)))
}
else{
showNotification(paste0("'", input$reduction_clustering_method, "' reduction not found in input object"))
}
}))
observeEvent(input$seuratFindMarkerSelectPhenotype, {
if(!is.null(vals$counts)){
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup1",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
)
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup2",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
)
}
})
observeEvent(input$seuratFindMarkerGroup1, {
if(!is.null(vals$counts)){
matchedIndex <- match(input$seuratFindMarkerGroup1, unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]]))
if(!is.na(matchedIndex)){
updateSelectInput(
session = session,
inputId = "seuratFindMarkerGroup2",
choices = unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])[-matchedIndex]
)
}
}
})
observeEvent(input$seuratFindMarkerRun, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding Marker Genes"))
if(input$seuratFindMarkerType == "markerAll"){
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
allGroup = input$seuratFindMarkerSelectPhenotype,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
else{
indices1 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup1, arr.ind = TRUE)
indices2 <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == input$seuratFindMarkerGroup2, arr.ind = TRUE)
cells1 <- colnames(vals$counts)[indices1]
cells2 <- colnames(vals$counts)[indices2]
if(input$seuratFindMarkerType == "markerConserved"){
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
cells1 = cells1,
cells2 = cells2,
group1 = input$seuratFindMarkerGroup1,
group2 = input$seuratFindMarkerGroup2,
conserved = TRUE,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
else{
vals$counts <- runSeuratFindMarkers(inSCE = vals$counts,
cells1 = cells1,
cells2 = cells2,
group1 = input$seuratFindMarkerGroup1,
group2 = input$seuratFindMarkerGroup2,
test = input$seuratFindMarkerTest,
onlyPos = input$seuratFindMarkerPosOnly)
}
}
shinyjs::show(selector = ".seurat_findmarker_table")
shinyjs::show(selector = ".seurat_findmarker_jointHeatmap")
shinyjs::show(selector = ".seurat_findmarker_plots")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
HTML(paste("<span style='color:red'>Select genes from the above table to plot!</span>"))
)
)
)
)
)
)
#df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
df <- metadata(vals$counts)$seuratMarkers
seuratObject <- convertSCEToSeurat(vals$counts, normAssay = "seuratNormData")
indices <- list()
cells <- list()
groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
for(i in seq(length(groups))){
indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
cells[[i]] <- colnames(vals$counts)[indices[[i]]]
cells[[i]] <- lapply(
X = cells[[i]],
FUN = function(t) gsub(
pattern = "_",
replacement = "-",
x = t,
fixed = TRUE)
)
if(seurat.version >= 5.0){
Idents(seuratObject, cells = unlist(cells[[i]])) <- groups[i]
}
else{
Idents(seuratObject, cells = cells[[i]]) <- groups[i]
}
}
showTab(inputId = "seuratFindMarkerPlotTabset", target = "Joint Heatmap Plot")
updateTabsetPanel(session = session, inputId = "seuratFindMarkerPlotTabset", selected = "Ridge Plot")
shinyjs::show(selector = ".seurat_findmarker_plots")
# Output the heatmap
colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
top10markers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=10)
# Subset seuratObject to contain only cells available in selected clusters
if(input$seuratFindMarkerType != "markerAll"){
subsetIdents <- c(unique(top10markers$cluster1), unique(top10markers$cluster2))
subsetIdents <- subsetIdents[subsetIdents!="all"]
seuratObject <- subset(seuratObject, idents = subsetIdents)
}
seuratObject <- Seurat::ScaleData(seuratObject, features = top10markers$gene.id)
# Plot heatmap
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
DoHeatmap(seuratObject, features = top10markers$gene.id)
})
})
# output$findMarkerHeatmapPlotFullTopText <- renderUI({
# h6(paste("Heatmap plotted across all groups against genes with adjusted p-values <", input$seuratFindMarkerPValAdjInput))
# })
message(paste0(date(), " ... Find Markers Complete"))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-seurat", parent = session,
de = TRUE, fm = TRUE, pa = TRUE)
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "success"))
updateCollapse(session = session, "SeuratUI", style = list("Downstream Analysis" = "info"))
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Ridge Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "seuratFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Ridge Plot",
panel(heading = "Ridge Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerRidgePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerFeaturePlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerDotPlot")
)
)
)
)
appendTab(inputId = "seuratFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "findMarkerHeatmapPlot")
)
)
)
)
)
)
#singleCellTK:::.exportMetaSlot(vals$counts, "seuratMarkers")
orderByLFCMarkers <- metadata(vals$counts)$seuratMarkers
orderByLFCMarkers <- orderByLFCMarkers[order(-orderByLFCMarkers$avg_log2FC), ]
vals$fts <- callModule(
module = filterTableServer,
id = "filterSeuratFindMarker",
dataframe = orderByLFCMarkers,
defaultFilterColumns = c("p_val_adj"),
defaultFilterOperators = c("<="),
defaultFilterValues = c("0.05"),
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
# vals$fts <- callModule(
# module = filterTableServer,
# id = "filterSeuratFindMarker",
# dataframe = orderByLFCMarkers
# )
}))
observeEvent(input$findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
msg = "Please wait while heatmap is being plotted. See console log for progress.",
{
##df <- metadata(vals$counts)$seuratMarkers[which(metadata(vals$counts)$seuratMarkers$p_val_adj < 0.05, arr.ind = TRUE),]
df <- metadata(vals$counts)$seuratMarkers
seuratObject <- convertSCEToSeurat(vals$counts, normAssay = "seuratNormData")
indices <- list()
cells <- list()
groups <- unique(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]])
for(i in seq(length(groups))){
indices[[i]] <- which(colData(vals$counts)[[input$seuratFindMarkerSelectPhenotype]] == groups[i], arr.ind = TRUE)
cells[[i]] <- colnames(vals$counts)[indices[[i]]]
cells[[i]] <- lapply(
X = cells[[i]],
FUN = function(t) gsub(
pattern = "_",
replacement = "-",
x = t,
fixed = TRUE)
)
if(seurat.version >= 5.0){
cells[[i]] = unlist(cells[[i]])
}
Idents(seuratObject, cells = cells[[i]]) <- groups[i]
}
colnames(df)[which(startsWith(colnames(df), "avg") == TRUE)] <- "avg_log2FC"
topMarkers <- df %>% group_by(cluster1) %>% arrange(desc(avg_log2FC)) %>% slice_head(n=input$findMarkerHeatmapPlotFullNumeric)
#topMarkers <- data.frame(df %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, avg_log2FC))
# if(nrow(topMarkers) > (input$findMarkerHeatmapPlotFullNumeric * length(groups))){
# topMarkers <- data.frame(topMarkers %>% group_by(cluster1) %>% top_n(input$findMarkerHeatmapPlotFullNumeric, -p_val_adj))
# }
# Subset seuratObject to contain only cells available in selected clusters
if(input$seuratFindMarkerType != "markerAll"){
subsetIdents <- c(unique(topMarkers$cluster1), unique(topMarkers$cluster2))
subsetIdents <- subsetIdents[subsetIdents!="all"]
seuratObject <- subset(seuratObject, idents = subsetIdents)
}
seuratObject <- Seurat::ScaleData(seuratObject, features = topMarkers$gene.id)
# Plot heatmap
output$findMarkerHeatmapPlotFull <- renderPlot({
isolate({
DoHeatmap(seuratObject, features = topMarkers$gene.id)
})
})
}))
observe({
req(vals$fts$data)
req(vals$fts$selectedRows)
df <- vals$fts$data[vals$fts$selectedRows, ]
output$findMarkerRidgePlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "ridge",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE
)
})
output$findMarkerViolinPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "violin",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE
)
})
output$findMarkerFeaturePlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "feature",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype,
ncol = 2,
combine = TRUE,
useReduction = input$seuratFeatureUseReduction
)
})
output$findMarkerDotPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "dot",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype
)
})
output$findMarkerHeatmapPlot <- renderPlot({
plotSeuratGenes(
inSCE = vals$counts,
plotType = "heatmap",
features = df$gene_id,
groupVariable = input$seuratFindMarkerSelectPhenotype
)
})
})
# observe({
# req(vals$fts$data)
# df <- vals$fts$data
# output$findMarkerHeatmapPlotFull <- renderPlot({
# plotSeuratGenes(
# inSCE = vals$counts,
# scaledAssayName = "seuratScaledData",
# plotType = "heatmap",
# features = df$gene_id,
# groupVariable = input$seuratFindMarkerSelectPhenotype
# )
# })
# })
#Update PCA/ICA message in clustering tab
output$display_message_clustering <- renderText({
if(input$reduction_clustering_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Run tSNE
observeEvent(input$run_tsne_button, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToTSNE = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_tsne_method]]
}
else
pathToTSNE = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_tsne_method]]
if(!is.null(pathToTSNE)){
message(paste0(date(), " ... Running tSNE"))
vals$counts <- runSeuratTSNE(inSCE = vals$counts,
useReduction = input$reduction_tsne_method,
reducedDimName = "seuratTSNE",
dims = input$pca_significant_pc_counter,
perplexity = input$perplexity_tsne,
seed = input$seed_TSNE)
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)
message(paste0(date(), " ... Plotting tSNE"))
output$plot_tsne <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "tsne",
showLegend = FALSE))
})
})
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... tSNE Complete"))
}
else{
showNotification(paste0("'", input$reduction_tsne_method, "' reduction not found in input object"))
}
}))
#Update PCA/ICA message in tSNE tab
output$display_message_tsne <- renderText({
if(input$reduction_tsne_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Run UMAP
observeEvent(input$run_umap_button, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
if(packageVersion(pkg = "SeuratObject") >= 5.0){
pathToUMAP = vals$counts@metadata$seurat$obj$"reductions"[[input$reduction_umap_method]]
}
else
pathToUMAP = vals$counts@metadata$seurat$obj@"reductions"[[input$reduction_umap_method]]
if(!is.null(pathToUMAP)){
message(paste0(date(), " ... Running UMAP"))
vals$counts <- runSeuratUMAP(inSCE = vals$counts,
useReduction = input$reduction_umap_method,
reducedDimName = "seuratUMAP",
dims = input$pca_significant_pc_counter,
minDist = input$min_dist_umap,
nNeighbors = input$n_neighbors_umap,
spread = input$spread_umap,
seed = input$seed_UMAP)
vals$counts <- singleCellTK:::.seuratInvalidate(inSCE = vals$counts, scaleData = FALSE, varFeatures = FALSE, PCA = FALSE, ICA = FALSE, tSNE = FALSE, UMAP = FALSE)
message(paste0(date(), " ... Plotting UMAP"))
output$plot_umap <- renderPlotly({
isolate({
plotly::ggplotly(plotSeuratReduction(inSCE = vals$counts,
useReduction = "umap",
showLegend = FALSE))
})
})
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#SeuratUI > div[value='Clustering']")
S4Vectors::metadata(vals$counts)$seuratMarkers <- NULL
shinyjs::hide(
selector = "div[value='Downstream Analysis']")
message(paste0(date(), " ... UMAP Complete"))
}
else{
showNotification(paste0("'", input$reduction_umap_method, "' reduction not found in input object"))
}
}))
#Update PCA/ICA message in UMAP tab
output$display_message_umap <- renderText({
if(input$reduction_umap_method == "pca"){
if(input$pca_significant_pc_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$pca_significant_pc_counter," components</span> from PCA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
else{ #ICA to do
if(input$ica_significant_ic_counter){
paste("<p>Analysis will be performed with <span style='color:red'>", input$ica_significant_ic_counter," components</span> from ICA. This number can be changed in the 'Dimensionality Reduction' section. </p>")
}
}
})
#Update pca significant slider maximum value with total number of computed principal components
observe({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$count_pc)) {
updateSliderInput(session = session, inputId = "pca_significant_pc_counter", max = vals$counts@metadata$seurat$count_pc)
}
})
#Update ica significant slider maximum value with total number of computed independent components
observe({
req(vals$counts)
if (!is.null(vals$counts@metadata$seurat$count_ic)) {
updateNumericInput(session = session, inputId = "ica_significant_ic_counter", max = vals$counts@metadata$seurat$count_ic)
}
})
#Update tsne, umap and clustering selected number of principal components input
observe({
if (input$reduction_umap_method == "pca") {
updateTextInput(session = session, inputId = "reduction_umap_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_umap_method == "ica") {
updateTextInput(session = session, inputId = "reduction_umap_count", value = vals$counts@metadata$seurat$count_ic)
}
if (input$reduction_clustering_method == "pca") {
updateTextInput(session = session, inputId = "reduction_clustering_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_clustering_method == "ica") {
updateTextInput(session = session, inputId = "reduction_clustering_count", value = vals$counts@metadata$seurat$count_ic)
}
if (input$reduction_tsne_method == "pca") {
updateTextInput(session = session, inputId = "reduction_tsne_count", value = input$pca_significant_pc_counter)
}
else if (input$reduction_tsne_method == "ica") {
updateTextInput(session = session, inputId = "reduction_tsne_count", value = vals$counts@metadata$seurat$count_ic)
}
})
#Customize heatmap (pca) with selected options
observeEvent(input$plot_heatmap_pca_button, {
if (!is.null(input$picker_dimheatmap_components_pca)) {
output$plot_heatmap_pca <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_pca,
dims = length(input$picker_dimheatmap_components_pca),
ncol = input$slider_dimheatmap_pca,
labels = input$picker_dimheatmap_components_pca)
})
})
}
session$sendCustomMessage("close_dropDownSeuratHM", "")
})
#Customize heatmap (ica) with selected options
observeEvent(input$plot_heatmap_ica_button, {
if (!is.null(input$picker_dimheatmap_components_ica)) {
output$plot_heatmap_ica <- renderPlot({
isolate({
plotSeuratHeatmap(plotObject = vals$counts@metadata$seurat$heatmap_ica,
dims = length(input$picker_dimheatmap_components_ica),
ncol = input$slider_dimheatmap_ica,
labels = input$picker_dimheatmap_components_ica)
})
})
}
})
#Disable Seurat tabs & reset collapse panel tabs
observe({
if(!is.null(vals$counts)){
#If data is uploaded in data tab, enable first tab i.e. Normalization tab in Seurat workflow
shinyjs::enable(
selector = "#SeuratUI > div[value='Normalize Data']")
shinyjs::enable(
selector = "#ScanpyUI > div[value='Normalize Data']")
#Proceed only if sce object has metadata slot
if(!is.null(vals$counts@metadata)){
if(packageVersion(pkg = "SeuratObject") >= 5.0){
#Proceed only if sce object has seurat object stored in metadata slot
if(!is.null(vals$counts@metadata$seurat$obj)){
#If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
if(length(vals$counts@metadata$seurat$obj$RNA$"var.features") <= 0){
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
}
#Proceed if reduction slot is present in seurat object in metadata slot
if("reductions" %in% names(vals$counts@metadata$seurat$obj)){
#If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
if(is.null(vals$counts@metadata$seurat$obj$reductions$pca)
&& is.null(vals$counts@metadata$seurat$obj$reductions$ica)){
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
}
#If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
if(is.null(vals$counts@metadata$seurat$obj$reductions$tsne)
&& is.null(vals$counts@metadata$seurat$obj$reductions$umap)){
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
}
#If seurat cluster information removed from sce object, reset Clustering tab
if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj$meta.data)){
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
}
}
}
}
else{
#Proceed only if sce object has seurat object stored in metadata slot
if(!is.null(vals$counts@metadata$seurat$obj)){
# #If seuratScaledData has been removed from sce object, reset Scale Data tab and reset/lock its next tab
# if(!"seuratScaledData" %in% expDataNames(vals$counts)){
# updateCollapse(session = session, "SeuratUI", style = list("Scale Data" = "primary"))
# updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
# shinyjs::disable(selector = "div[value='Dimensionality Reduction']")
# }
#If variableFeatures have been removed from sce object, reset HVG tab and reset/lock next tab
if(length(slot(vals$counts@metadata$seurat$obj, "assays")[["RNA"]]@var.features) <= 0){
updateCollapse(session = session, "SeuratUI", style = list("Highly Variable Genes" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Dimensionality Reduction']")
}
#Proceed if reduction slot is present in seurat object in metadata slot
if("reductions" %in% slotNames(vals$counts@metadata$seurat$obj)){
#If PCA and ICA both removed from sce object, reset DR tab and reset/lock next tab
if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["pca"]])
&& is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["ica"]])){
updateCollapse(session = session, "SeuratUI", style = list("Dimensionality Reduction" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='2D-Embedding']")
}
#If TSNE and UMAP both removed from sce object, reset 2D-Embedding tab and reset/lock next tab
if(is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["tsne"]])
&& is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[["umap"]])){
updateCollapse(session = session, "SeuratUI", style = list("2D-Embedding" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Clustering']")
}
#If seurat cluster information removed from sce object, reset Clustering tab
if(!"seurat_clusters" %in% names(vals$counts@metadata$seurat$obj@meta.data)){
updateCollapse(session = session, "SeuratUI", style = list("Clustering" = "primary"))
updateCollapse(session = session, "SeuratUI", style = list("Find Markers" = "primary"))
shinyjs::disable(selector = "#SeuratUI > div[value='Find Markers']")
}
}
}
}
}
}
else{
#If no data uploaded in data tab, disable all tabs and plots.
#Disable tabs
shinyjs::disable(
selector = "#SeuratUI > div[value='Normalize Data']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Normalize Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Highly Variable Genes']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Highly Variable Genes']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Scale Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Dimensionality Reduction']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
shinyjs::disable(
selector = "#SeuratUI > div[value='2D-Embedding']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='2D-Embedding']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Clustering']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Clustering']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Scale Data']")
shinyjs::disable(
selector = "#SeuratUI > div[value='Find Markers']")
shinyjs::disable(
selector = "#ScanpyUI > div[value='Find Markers']")
#Disable plots inside PCA subtab
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='PCA Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='Elbow Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='JackStraw Plot']")
shinyjs::disable(
selector = ".seurat_pca_plots a[data-value='Heatmap Plot']")
#Disable plots inside ICA subtab
shinyjs::disable(
selector = ".seurat_ica_plots a[data-value='ICA Plot']")
shinyjs::disable(
selector = ".seurat_ica_plots a[data-value='Heatmap Plot']")
#Disabled plots inside Clustering tab
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='PCA Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='ICA Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='tSNE Plot']")
shinyjs::disable(
selector = ".seurat_clustering_plots a[data-value='UMAP Plot']")
}
})
#-----------------------------------------------------------------------------
# Page: Column Annotation (colData) ####
#-----------------------------------------------------------------------------
#populate colData from sce object when uploaded
observe({
if(!is.null(vals$counts)){
if(!is.null(colData(vals$counts))){
vals$columnAnnotation <- as.data.frame(colData(vals$counts))
}
}
})
#import colData from local storage
observeEvent(input$importDataButton_colData, {
withBusyIndicatorServer("importDataButton_colData",{
if(!is.null(input$uploadFile_colData)){
temp <- read.csv(input$uploadFile_colData$datapath, header = TRUE,sep = ",")
if(nrow(colData(vals$counts)) == nrow(temp)){
if(input$editorChoiceRadio_colData == "replace"){
vals$columnAnnotation <- temp
}
else{
x <- as.data.frame(colData(vals$counts))
y <- as.data.frame(temp)
commonCols <- intersect(colnames(x), colnames(y))
x[, commonCols] <- y[,commonCols]
y[, commonCols] <- NULL
vals$columnAnnotation <- cbind(x, y)
}
}
else{
showNotification("Number of rows of the assay and the input colData must be equal", type = "error")
}
}
else{
showNotification("No file selected to upload", type = "error")
}
})
#Render a warning message if there are unsaved changes to colData
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#Render table with colData
output$outputColumnAnnotationTable_colData <- renderUI({
output$colOutTable <- DT::renderDataTable({
DT::datatable(vals$columnAnnotation,
editable = 'cell',
options = list(pageLength = 5,
scrollX = TRUE))
})
DT::dataTableOutput("colOutTable")
})
#create selectinput for selecting attribute with colnames from incoming dataset
#create selectinput for selecting attribute value
output$inputSelectAttribute_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttribute_colData",
label = "select attribute",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeDelete_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeDelete_colData",
label = "select attribute to delete",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting column to delete
output$inputSelectAttributeValue_colData <- renderUI({
if(!is.null(vals$columnAnnotation) &&
ncol(vals$columnAnnotation) > 0 &&
!is.null(input$inputSelectAttribute_colData) &&
input$inputSelectAttribute_colData %in% colnames(vals$columnAnnotation)){
selectInput("inputSelectAttributeValue_colData",
label = "select attribute value",
choices = vals$columnAnnotation[, input$inputSelectAttribute_colData])
}
})
#create selectinput for selecting merge_1 attribute
#create selectinput for selecting merge_2 attribute
output$inputSelectAttributeMerge1_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeMerge1_colData",
label = "select first column",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeMerge2_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeMerge2_colData",
label = "select second column",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting fill_1 attribute
#create selectinput for selecting fill_2 attribute
output$inputSelectAttributeFill1_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFill1_colData",
label = "select attribute column",
choices = colnames(vals$columnAnnotation))
}
}
})
output$inputSelectAttributeFill2_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFill2_colData",
label = "select column to fill",
choices = colnames(vals$columnAnnotation))
}
}
})
#create selectinput for selecting attribute value for magic fill
output$inputSelectAttributeFillvalue_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeFillvalue_colData",
label = "select attribute value",
choices = vals$columnAnnotation[, match(input$inputSelectAttributeFill1_colData,
colnames(vals$columnAnnotation))])
}
}
})
#update criteria parameter text input when attribute value selectinput is changed
observeEvent(input$inputSelectAttributeValue_colData, {
updateTextInput(session = session,
"inputCriteria_colData",
value = input$inputSelectAttributeValue_colData)
})
#create selectinput for selecting attribute for clean operation
output$inputSelectAttributeClean_colData <- renderUI({
if(!is.null(vals$columnAnnotation)){
if(ncol(vals$columnAnnotation) > 0){
selectInput("inputSelectAttributeClean_colData",
label = "select attribute column",
choices = colnames(vals$columnAnnotation))
}
}
})
#confirm create bin button
observeEvent(input$buttonConfirmBin_colData, {
#getting variables
selected_attribute <- input$inputSelectAttribute_colData
bin_name <- input$inputBinName_colData
selected_column_no <- match(selected_attribute, colnames(vals$columnAnnotation))
criteria_term <- input$inputCriteria_colData
operator_term <- input$inputOperator_colData
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operations
if (operator_term == "=")
{
df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
}
else if (operator_term == ">")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
}
else if (operator_term == "<")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
}
else if (operator_term == "<=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
}
else if (operator_term == ">=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm merge button
observeEvent(input$buttonConfirmMerge_colData, {
df <- vals$columnAnnotation
colname1 <- input$inputSelectAttributeMerge1_colData
colname2 <- input$inputSelectAttributeMerge2_colData
df <- unite_(df, col = colname1, c(colname1, colname2),
sep = input$inputSelectSeparatorMerge_colData)
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#fill column button
observeEvent(input$buttonConfirmFill_colData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operation
selected_attribute_1 <- input$inputSelectAttributeFill1_colData
selected_attribute_2 <- input$inputSelectAttributeFill2_colData
selected_column_no_1 <- match(selected_attribute_1, colnames(df))
selected_column_no_2 <- match(selected_attribute_2, colnames(df))
old_value <- input$inputSelectAttributeFillvalue_colData
new_value <- input$inputReplaceText_colData
for (i in 1:nrow(df))
{
if (df[i, selected_column_no_1] == old_value)
{
df[i, selected_column_no_2] <- new_value
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm clean button
observeEvent(input$buttonConfirmClean_colData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$columnAnnotation, operation = "backup")
df <- data$df
#perform operation
selected_attribute <- input$inputSelectAttributeClean_colData
selected_column_no <- match(selected_attribute, colnames(df))
selected_choice <- input$inputRemovalOperation_colData
selected_choice_no <- match(selected_choice, c("remove alphabets",
"remove digits",
"remove spaces",
"remove symbols"))
if (selected_choice_no == 1)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 2)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 3)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 4)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$columnAnnotation <- data$df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#add new empty column button
observeEvent(input$buttonConfirmEmptyColumnName_colData, {
df <- vals$columnAnnotation
colname <- input$inputEmptyColumnName_colData
df$newcolumn <- input$inputDefaultValueAddColumn_colData
names(df)[ncol(df)] <- colname
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#delete column button
observeEvent(input$buttonConfirmDeleteColumn_colData,{
#getting variables
selected_attribute <- input$inputSelectAttributeDelete_colData
#get df from reactive input
df <- vals$columnAnnotation
#delete
df[[selected_attribute]] <- NULL
vals$columnAnnotation <- df
output$changesWarning_colData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#restore saved/original colData
observeEvent(input$buttonRestore_colData,{
vals$columnAnnotation <- as.data.frame(colData(vals$counts))
output$changesWarning_colData <- NULL
showNotification("Changes reverted back to last checkpoint.")
})
#save changes to colData
observeEvent(input$buttonSave_colData,{
colData(vals$counts) <- DataFrame(vals$columnAnnotation)
output$changesWarning_colData <- NULL
updateColDataNames()
showNotification("Changes saved successfully.")
})
#-----------------------------------------------------------------------------
# Page: Row Annotation (rowData) ####
#-----------------------------------------------------------------------------
#populate colData from sce object when uploaded
observe({
if(!is.null(vals$counts)){
if(!is.null(rowData(vals$counts))){
vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
}
}
})
#import rowData from local storage
observeEvent(input$importDataButton_rowData, {
withBusyIndicatorServer("importDataButton_rowData",{
if(!is.null(input$uploadFile_rowData)){
temp <- read.csv(input$uploadFile_rowData$datapath, header = TRUE,sep = ",")
if(nrow(rowData(vals$counts)) == nrow(temp)){
if(input$editorChoiceRadio_rowData == "replace"){
vals$rowAnnotation <- temp
}
else{
x <- as.data.frame(rowData(vals$counts))
y <- as.data.frame(temp)
commonCols <- intersect(colnames(x), colnames(y))
x[, commonCols] <- y[,commonCols]
y[, commonCols] <- NULL
vals$rowAnnotation <- cbind(x, y)
}
}
else{
showNotification("Number of rows of the assay and the input rowData must be equal", type = "error")
}
}
else{
showNotification("No file selected to upload", type = "error")
}
})
#Render a warning message if there are unsaved changes to rowData
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#Render table with rowData
output$outputColumnAnnotationTable_rowData <- renderUI({
output$rowOutTable <- DT::renderDataTable({
DT::datatable(vals$rowAnnotation,
editable = 'cell',
options = list(pageLength = 5,
scrollX = TRUE))
})
DT::dataTableOutput("rowOutTable")
})
#create selectinput for selecting attribute with colnames from incoming dataset
#create selectinput for selecting attribute value
output$inputSelectAttribute_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttribute_rowData",
label = "select attribute",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeDelete_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeDelete_rowData",
label = "select attribute to delete",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting column to delete
observeEvent(input$inputSelectAttribute_rowData, {
if(!is.null(vals$rowAnnotation) &&
ncol(vals$rowAnnotation) > 0 &&
!is.null(input$inputSelectAttribute_rowData) &&
input$inputSelectAttribute_rowData %in% colnames(vals$rowAnnotation)){
updateSelectizeInput(session, "inputSelectAttributeValue_rowData",
choices = vals$rowAnnotation[, input$inputSelectAttribute_rowData],
server = TRUE)
}
})
#create selectinput for selecting merge_1 attribute
#create selectinput for selecting merge_2 attribute
output$inputSelectAttributeMerge1_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeMerge1_rowData",
label = "select first column",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeMerge2_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeMerge2_rowData",
label = "select second column",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting fill_1 attribute
#create selectinput for selecting fill_2 attribute
output$inputSelectAttributeFill1_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeFill1_rowData",
label = "select attribute column",
choices = colnames(vals$rowAnnotation))
}
}
})
output$inputSelectAttributeFill2_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeFill2_rowData",
label = "select column to fill",
choices = colnames(vals$rowAnnotation))
}
}
})
#create selectinput for selecting attribute value for magic fill
observeEvent(input$inputSelectAttributeFill1_rowData, {
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
updateSelectizeInput(session, "inputSelectAttributeFillvalue_rowData",
choices = vals$rowAnnotation[, match(input$inputSelectAttributeFill1_rowData,
colnames(vals$rowAnnotation))],
server = TRUE)
}
}
})
#update criteria parameter text input when attribute value selectinput is changed
observeEvent(input$inputSelectAttributeValue_rowData, {
updateTextInput(session = session,
"inputCriteria_rowData",
value = input$inputSelectAttributeValue_rowData)
})
#create selectinput for selecting attribute for clean operation
output$inputSelectAttributeClean_rowData <- renderUI({
if(!is.null(vals$rowAnnotation)){
if(ncol(vals$rowAnnotation) > 0){
selectInput("inputSelectAttributeClean_rowData",
label = "select attribute column",
choices = colnames(vals$rowAnnotation))
}
}
})
#confirm create bin button
observeEvent(input$buttonConfirmBin_rowData, {
#getting variables
selected_attribute <- input$inputSelectAttribute_rowData
bin_name <- input$inputBinName_rowData
selected_column_no <- match(selected_attribute, colnames(vals$rowAnnotation))
criteria_term <- input$inputCriteria_rowData
operator_term <- input$inputOperator_rowData
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
if (operator_term == "=")
{
df[, selected_column_no][df[, selected_column_no] %in% criteria_term] <- bin_name
}
else if (operator_term == ">")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) > criteria_term] <- bin_name
}
else if (operator_term == "<")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) < criteria_term] <- bin_name
}
else if (operator_term == "<=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) <= criteria_term] <- bin_name
}
else if (operator_term == ">=")
{
df[, selected_column_no][as.numeric(df[, selected_column_no]) >= criteria_term] <- bin_name
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm merge button
observeEvent(input$buttonConfirmMerge_rowData, {
df <- vals$rowAnnotation
colname1 <- input$inputSelectAttributeMerge1_rowData
colname2 <- input$inputSelectAttributeMerge2_rowData
df <- unite_(df, col = colname1, c(colname1, colname2),
sep = input$inputSelectSeparatorMerge_rowData)
vals$rowAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#fill column button
observeEvent(input$buttonConfirmFill_rowData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
selected_attribute_1 <- input$inputSelectAttributeFill1_rowData
selected_attribute_2 <- input$inputSelectAttributeFill2_rowData
selected_column_no_1 <- match(selected_attribute_1, colnames(df))
selected_column_no_2 <- match(selected_attribute_2, colnames(df))
old_value <- input$inputSelectAttributeFillvalue_rowData
new_value <- input$inputReplaceText_rowData
for (i in 1:nrow(df))
{
if (df[i, selected_column_no_1] == old_value)
{
df[i, selected_column_no_2] <- new_value
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#confirm clean button
observeEvent(input$buttonConfirmClean_rowData, {
#get df from reactive input, backup column datatypes and convert factor to character
data <- singleCellTK:::.manageFactor(vals$rowAnnotation, operation = "backup")
df <- data$df
#operations
selected_attribute <- input$inputSelectAttributeClean_rowData
selected_column_no <- match(selected_attribute, colnames(df))
selected_choice <- input$inputRemovalOperation_rowData
selected_choice_no <- match(selected_choice, c("remove alphabets",
"remove digits",
"remove spaces",
"remove symbols"))
if (selected_choice_no == 1)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[A-z]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 2)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[0-9]", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 3)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub(" ", "", df[i, selected_column_no])
}
}
else if (selected_choice_no == 4)
{
for (i in 1:nrow(df))
{
df[i, selected_column_no] <- gsub("[[:punct:]]", "", df[i, selected_column_no])
}
}
#restore datatypes
data$df <- df
data <- singleCellTK:::.manageFactor(data, operation = "restore")
vals$rowAnnotation <- data$df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#add new empty column button
observeEvent(input$buttonConfirmEmptyColumnName_rowData, {
df <- vals$rowAnnotation
colname <- input$inputEmptyColumnName_rowData
df$newcolumn <- input$inputDefaultValueAddColumn_rowData
names(df)[ncol(df)] <- colname
vals$columnAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#delete column button
observeEvent(input$buttonConfirmDeleteColumn_rowData,{
#getting variables
selected_attribute <- input$inputSelectAttributeDelete_rowData
#get df from reactive input
df <- vals$rowAnnotation
#delete
df[[selected_attribute]] <- NULL
vals$rowAnnotation <- df
output$changesWarning_rowData <- renderUI({
HTML("<h5><span style='color:red'> You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.</span></h5></br>")
})
showNotification("You have made changes to the Cell Annotation data. Select 'Save' to finalize these changes or 'Reset' to discard the changes.", type = "error")
})
#restore saved/original rowData
observeEvent(input$buttonRestore_rowData,{
vals$rowAnnotation <- as.data.frame(rowData(vals$counts))
output$changesWarning_rowData <- NULL
showNotification("Changes reverted back to last checkpoint.")
})
#save changes to rowData
observeEvent(input$buttonSave_rowData,{
rowData(vals$counts) <- DataFrame(vals$rowAnnotation)
output$changesWarning_rowData <- NULL
updateFeatureAnnots()
showNotification("Changes saved successfully.")
})
#-----------------------------------------------------------------------------
# Page Download ####
#-----------------------------------------------------------------------------
output$exportFileName <- renderUI({
defaultName <- paste0("SCE-", strftime(Sys.time(), format = "%y%m%d_%H%M"))
if (input$exportChoice == "rds") {
extName <- ".rds"
} else if (input$exportChoice == "annData") {
extName <- ".h5ad"
}
tags$div(
div(style = "display: inline-block;vertical-align:top; width: 160px;",
textInput("exportPrefix", label = NULL,
value = defaultName, placeholder = "Required!",
width = '160px')),
div(
style = "display: inline-block;vertical-align:top; width: 50px;",
p(extName, style = "margin-top: 8px; margin-left: 2px; font-size: 16px;")
)
)
})
addPopover(session, 'exportAssayLabel', '', "The name of assay of interests that will be set as the primary matrix of the output AnnData.", 'right')
output$exportData <- downloadHandler(
filename = function() {
if (input$exportChoice == "rds") {
paste0(input$exportPrefix, ".rds")
}
else if (input$exportChoice == "annData"){
paste0(input$exportPrefix, ".h5ad")
}
},
content = function(file) {
if (input$exportChoice == "rds") {
saveRDS(vals$counts, file)
}
else if (input$exportChoice == "annData") {
zellkonverter::writeH5AD(sce = vals$counts,
file,
X_name = input$exportAssay)
}
}
)
##############################################################################
# Page: Cell Type Labeling ####
##############################################################################
output$ctLabelLevelUI <- renderUI({
if (input$ctLabelRef %in% c("hpca", "bpe", "dice", "immgen", "mouse")) {
selectInput("ctLabelLevel", "Labeling level:",
c("main", "fine", "ont"), "main")
} else {
disabled(
selectInput("ctLabelLevel", "Labeling level (not supported):",
choices = NULL, selected = NULL)
)
}
})
observeEvent(input$ctLabelRun, withConsoleMsgRedirect(
msg = "Please wait while cell types are being labeled. See console log for progress.",
{
req(vals$counts)
if (input$ctLabelBy == "Clusters") {
cluster <- input$ctLabelByCluster
if (is.null(cluster) || cluster == "") {
stop("Choose the clustering label for this condition!")
}
} else {
cluster <- NULL
}
vals$counts <- runSingleR(vals$counts,
useAssay = input$ctLabelAssay,
useBltinRef = input$ctLabelRef,
level = input$ctLabelLevel,
featureType = input$ctLabelFeatureType,
labelByCluster = cluster)
updateColDataNames()
message(date(), " ... SingleR finished")
}
))
##############################################################################
# Page: Scanpy Curated Workflow ####
##############################################################################
# Run Normalization
observeEvent(input$scanpy_normalize_button, withConsoleMsgRedirect(
msg = "Please wait while data is being normalized. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Normalizing Data"))
vals$counts <- runScanpyNormalizeData(inSCE = vals$counts,
useAssay = input$scanpySelectNormalizationAssay,
targetSum = input$scanpy_targetSum,
maxFraction = input$scanpy_maxFraction,
normAssayName = "scanpyNormData"
)
updateCollapse(session = session, "ScanpyUI", style = list("Normalize Data" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Highly Variable Genes']")
updateAssayInputs()
message(paste0(date(), " ... Normalization Complete"))
}))
# HVG
#Find HVG
observeEvent(input$scanpy_find_hvg_button, withConsoleMsgRedirect(
msg = "Please wait while high variable genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding High Variable Genes"))
useAssay <- NULL
maxDisp <- NULL
if(input$scanpy_maxDisp_Inf){
maxDisp <- Inf
}
else{
maxDisp <- input$scanpy_maxDisp
}
vals$counts <- runScanpyFindHVG(inSCE = vals$counts,
useAssay = "scanpyNormData",
method = input$scanpy_hvg_method,
hvgNumber = input$scanpy_hvg_no_features,
minMean = input$scanpy_minMean,
maxMean = input$scanpy_maxMean,
minDisp = input$scanpy_minDisp,
maxDisp = maxDisp)
message(paste0(date(), " ... Finding HVG Complete"))
updateCollapse(session = session, "ScanpyUI", style = list("Highly Variable Genes" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Dimensionality Reduction']")
message(paste0(date(), " ... Plotting HVG"))
output$scanpy_plot_hvg <- renderPlot({
isolate({
plotScanpyHVG(vals$counts)
})
})
message(paste0(date(), " ... Finding HVG Complete"))
}
))
# PCA
observeEvent(input$scanpy_run_pca_button, withConsoleMsgRedirect(
msg = "Please wait while PCA is being computed. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Running PCA"))
vals$counts <- runScanpyScaleData(inSCE = vals$counts,
useAssay = "scanpyNormData")
vals$counts <- runScanpyPCA(inSCE = vals$counts,
useAssay = "scanpyScaledData",
method = input$scanpy_pca_method,
nPCs = input$scanpy_pca_no_components,
reducedDimName = "scanpyPCA",
use_highly_variable = TRUE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Variance",
panel(heading = "PCA Variance",
plotOutput(outputId = "scanpy_plot_pca_variance")
)
), select = TRUE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotOutput(outputId = "scanpy_plot_pca")
)
), select = FALSE)
appendTab(inputId = "scanpyPCAPlotTabset", tabPanel(title = "PCA Gene Ranking",
panel(heading = "PCA Gene Ranking",
plotOutput(outputId = "scanpy_plot_pca_gene_ranking")
)
), select = FALSE)
message(paste0(date(), " ... Plotting PCA"))
output$scanpy_plot_pca_variance <- renderPlot({
isolate({
plotScanpyPCAVariance(inSCE = vals$counts, log = TRUE)
})
})
output$scanpy_plot_pca_gene_ranking <- renderPlot({
isolate({
plotScanpyPCAGeneRanking(inSCE = vals$counts)
})
})
output$scanpy_plot_pca <- renderPlot({
isolate({
plotScanpyPCA(inSCE = vals$counts)
})
})
updateCollapse(session = session, "ScanpyUI", style = list("Dimensionality Reduction" = "success"))
shinyjs::enable(
selector = "#ScanpyUI > div[value='2D-Embedding']")
shinyjs::show(selector = ".scanpy_pca_plots")
message(paste0(date(), " ... PCA Complete"))
}))
# TSNE
observeEvent(input$scanpy_run_tsne_button, withConsoleMsgRedirect(
msg = "Please wait while tSNE is being computed. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Running tSNE"))
vals$counts <- runScanpyTSNE(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
# useAssay = "scanpyScaledData",
reducedDimName = "scanpyTSNE",
dims = input$scanpy_pca_significant_pc_counter,
perplexity = input$scanpy_perplexity_tsne)
message(paste0(date(), " ... Plotting tSNE"))
output$scanpy_plot_tsne <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyTSNE")
})
})
updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
message(paste0(date(), " ... tSNE Complete"))
}))
#UMAP
observeEvent(input$scanpy_run_umap_button, withConsoleMsgRedirect(
msg = "Please wait while UMAP is being computed. See console log for progress.",
{
req(vals$counts)
# if(!is.null(slot(vals$counts@metadata$seurat$obj, "reductions")[[input$reduction_umap_method]])){
message(paste0(date(), " ... Running UMAP"))
vals$counts <- runScanpyUMAP(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
# useAssay = "scanpyScaledData", #not needed when using PCA
reducedDimName = "scanpyUMAP",
dims = input$scanpy_pca_significant_pc_counter,
minDist = input$scanpy_min_dist_umap,
nNeighbors = input$scanpy_n_neighbors_umap,
spread = input$scanpy_spread_umap,
alpha = input$scanpy_spread_alpha,
gamma = input$scanpy_spread_gamma)
message(paste0(date(), " ... Plotting UMAP"))
output$scanpy_plot_umap <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyUMAP")
})
})
updateCollapse(session = session, "ScanpyUI", style = list("2D-Embedding" = "success"))
shinyjs::enable(selector = "#ScanpyUI > div[value='Clustering']")
message(paste0(date(), " ... UMAP Complete"))
}))
#Clustering
observeEvent(input$scanpy_find_clusters_button, withConsoleMsgRedirect(
msg = "Please wait while clusters are being computed. See console log for progress.",
{
req(vals$counts)
#Remove plot tabs if generated before
removeTab(inputId = "scanpyClusteringPlotTabset", target = "PCA Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "ICA Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "tSNE Plot")
removeTab(inputId = "scanpyClusteringPlotTabset", target = "UMAP Plot")
message(paste0(date(), " ... Clustering Dataset"))
vals$counts <- runScanpyFindClusters(inSCE = vals$counts,
useReducedDim = "scanpyPCA",
nNeighbors = input$scanpy_nNeighbors,
dims = input$scanpy_pca_significant_pc_counter,
method = input$scanpy_algorithm.use,
resolution = input$scanpy_resolution_clustering,
niterations = -1,
cor_method = input$scanpy_corr_method)
updateCollapse(session = session, "ScanpyUI", style = list("Clustering" = "success"))
message(paste0(date(), " ... Finding Clusters Complete"))
if("scanpyTSNE" %in% reducedDimNames(vals$counts)){
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "tSNE Plot",
panel(heading = "tSNE Plot",
plotOutput(outputId = "scanpy_plot_tsne_clustering")
)
)
)
message(paste0(date(), " ... Re-generating tSNE Plot with Cluster Labels"))
output$scanpy_plot_tsne_clustering <- renderPlot({
isolate({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyTSNE",
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
})
})
}
if("scanpyUMAP" %in% reducedDimNames(vals$counts)){
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "UMAP Plot",
panel(heading = "UMAP Plot",
plotOutput(outputId = "scanpy_plot_umap_clustering")
)
), select = TRUE
)
message(paste0(date(), " ... Re-generating UMAP Plot with Cluster Labels"))
output$scanpy_plot_umap_clustering <- renderPlot({
isolate({
plotScanpyEmbedding(
inSCE = vals$counts,
reducedDimName = "scanpyUMAP",
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering)
)
})
})
}
appendTab(inputId = "scanpyClusteringPlotTabset", tabPanel(title = "PCA Plot",
panel(heading = "PCA Plot",
plotOutput(outputId = "scanpy_plot_pca_clustering")
)
), select = FALSE)
message(paste0(date(), " ... Re-generating PCA Plot with Cluster Labels"))
output$scanpy_plot_pca_clustering <- renderPlot({
isolate({
plotScanpyPCA(inSCE = vals$counts,
color = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
})
})
shinyjs::show(selector = ".scanpy_clustering_plots")
#enable find marker selection
shinyjs::enable(
selector = "#ScanpyUI > div[value='Find Markers']")
#update colData names
updateColDataNames()
#populate updated colData items for findMarkers tab
updateSelectInput(session = session,
inputId = "scanpyFindMarkerSelectPhenotype",
choices = colnames(colData(vals$counts)),
selected = paste0(
"Scanpy_",
input$scanpy_algorithm.use,
"_",
input$scanpy_resolution_clustering))
}))
observeEvent(input$scanpyFindMarkerRun, withConsoleMsgRedirect(
msg = "Please wait while marker genes are being found. See console log for progress.",
{
req(vals$counts)
message(paste0(date(), " ... Finding Marker Genes"))
vals$counts <- runScanpyFindMarkers(inSCE = vals$counts,
colDataName = input$scanpyFindMarkerSelectPhenotype,
test = input$scanpyFindMarkerTest,
corr_method = input$scanpyFindMarkerCorrMethod)
shinyjs::show(selector = ".scanpy_findmarker_table")
shinyjs::show(selector = ".scanpy_findmarker_jointHeatmap")
shinyjs::show(selector = ".scanpy_findmarker_plots")
# Plot heatmap
output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
isolate({
plotScanpyMarkerGenesHeatmap(vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
nGenes = 2)
})
})
message(paste0(date(), " ... Find Markers Complete"))
# Show downstream analysis options
callModule(module = nonLinearWorkflow, id = "nlw-scanpy", parent = session,
de = TRUE, fm = TRUE, pa = TRUE)
updateCollapse(session = session, "ScanpyUI", style = list("Find Markers" = "success"))
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Matrix Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Violin Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Feature Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Dot Plot")
removeTab(inputId = "scanpyFindMarkerPlotTabset", target = "Heatmap Plot")
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Feature Plot",
panel(heading = "Feature Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerFeaturePlot")
)
)
), select = TRUE
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Violin Plot",
panel(heading = "Violin Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerViolinPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Matrix Plot",
panel(heading = "Matrix Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerMatrixPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Dot Plot",
panel(heading = "Dot Plot",
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpyFindMarkerDotPlot")
)
)
)
)
appendTab(inputId = "scanpyFindMarkerPlotTabset",
tabPanel(title = "Heatmap Plot",
panel(heading = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
plotOutput(outputId = "scanpyFindMarkerHeatmapPlot")
)
)
)
)
)
)
vals$ftScanpy <- callModule(
module = filterTableServer,
id = "filterScanpyFindMarker",
dataframe = metadata(vals$counts)$scanpyMarkers,
defaultFilterColumns = c("Pvalue"),
defaultFilterOperators = c("<="),
defaultFilterValues = c("0.05"),
topText = "You can view the marker genes in the table below and apply custom filters to filter the table accordingly. A joint heatmap for all the marker genes available in the table is plotted underneath the table. Additional visualizations are plotted for select genes which can be selected by clicking on the rows of the table."
)
}))
observeEvent(input$scanpy_findMarkerHeatmapPlotFullNumericRun, withConsoleMsgRedirect(
msg = "Please wait while heatmap is being plotted. See console log for progress.",
{
output$scanpy_findMarkerHeatmapPlotFull <- renderPlot({
isolate({
plotScanpyMarkerGenesHeatmap(vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
nGenes = input$scanpy_findMarkerHeatmapPlotFullNumeric)
})
})
}))
observe({
req(vals$ftScanpy$data)
req(vals$ftScanpy$selectedRows)
df <- vals$ftScanpy$data[vals$ftScanpy$selectedRows, ]
output$scanpyFindMarkerFeaturePlot <- renderPlot({
plotScanpyEmbedding(inSCE = vals$counts,
reducedDimName = "scanpyUMAP",
color = df$Gene)
})
output$scanpyFindMarkerViolinPlot <- renderPlot({
plotScanpyViolin(inSCE = vals$counts,
features = df$Gene,
groupBy = input$scanpyFindMarkerSelectPhenotype,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerMatrixPlot <- renderPlot({
plotScanpyMatrixPlot(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerDotPlot <- renderPlot({
plotScanpyDotPlot(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
output$scanpyFindMarkerHeatmapPlot <- renderPlot({
plotScanpyHeatmap(inSCE = vals$counts,
groupBy = input$scanpyFindMarkerSelectPhenotype,
features = df$Gene,
useAssay = "scanpyNormData")
})
})
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.