inst/shiny/vizection/ui.R
In shamansim/Vizection: Visualisation and dissection of single-cell transcriptomics big datasets
# loading required library
library(shiny)
library(shinydashboard)
library(ade4)
library(adegraphics)
library(magrittr)
library(dplyr)
library(ggplot2)
library(data.table)
library(colorspace)
library(smallCAGEqc)
library(dendextend)
library(DT)
if (getOption("vizection.dataIs") == "genesAndLibs") {
genes <- get(getOption("vizection.genes"), .GlobalEnv)
libs <- get(getOption("vizection.libs"), .GlobalEnv)
} else if (getOption("vizection.dataIs") == "se") {
se <- get(getOption("vizection.se"), .GlobalEnv)
genes <- SummarizedExperiment::assay(se) %>% data.frame
libs <- SummarizedExperiment::colData(se) %>% data.frame(stringsAsFactors = FALSE)
libs$samplename <- rownames(libs)
libs$counts <- colSums(genes)
} else stop("Could not detect what data to load.")
if (is.null(libs$group))
libs$group <- "No groups"
libs$group %<>% factor
# BEGIN shiny app
dashboardPage(
# HEADER
# ------
dashboardHeader(title = tags$img(src = "vizection_white.png", alt = "Vizection", height = "40"),
dropdownMenuOutput("tasksMenu")
),#dashboardHeader
# SIDEBAR
# -------
dashboardSidebar(#width = 450,
sidebarMenu(id = "sidebarmenu",
menuItem("Selection", tabName = "selection", icon = icon("check-square-o")),
menuItem("General", tabName = "home", icon = icon("home")),
menuItem("Clustering", tabName = "clustering", icon = icon("sort-amount-desc"),
menuSubItem("Hierarchical clustering", tabName = "hierarchicalClustering", icon = icon("sitemap")),
menuSubItem("K-means", tabName = "k-means", icon = icon("dot-circle-o"))
),#clustering
menuItem("PCA", tabName = "PCA", icon = icon("area-chart")
),#PCA
menuItem("CA", tabName = "CA", icon = icon("area-chart")
),#CA
menuItem("Export", tabName = "export", icon = icon("share-square-o")
),#Export
menuItem("About", tabName = "about", icon = icon("question-circle")
)#About
)#sidebarMenu
),#dashboardSidebar
# BODY
# ----
dashboardBody(
tags$head(
tags$style(HTML('
/*
body
*/
body {
height: auto;
overflow: auto;
font-family: sans-serif;
}
/*
header
*/
.skin-blue .main-header .logo {
background-color: black;
}
.skin-blue .main-header .navbar {
background-color: black;
}
/*
sidebar
*/
.skin-blue .left-side, .skin-blue .main-sidebar, .skin-blue .wrapper {
background-color: black;
}
.skin-blue .sidebar-menu > li.active > a, .skin-blue .sidebar-menu > li:hover > a {
background: black;
}
/*
content
*/
.content {
background-color: white;
}
.content-wrapper, .right-side {
background-color: white;
}
img {
display: block;
margin: auto;
}
.fa-5x {
text-align: center;
width: 100%;
}
.titre {
text-align: center;
width: 100%;
font-family: sans-serif;
font-variant: small-caps;
}
/* tabs */
.nav > li > a:active, .nav > li > a:focus, .nav > li > a:hover {
background: #ff9d00;
}
/* boxplotGeneral */
#boxplotGeneral {
text-align: center;
}
/* label above input (e.g. dendrogram nb clusters) */
.centeredInput {
text-align: center;
display: block;
margin: auto;
width: 10%;
}
/* solid boxes primary and info */
.box.box-solid.box-primary > .box-header {
background-color: #111987;
}
.box.box-solid.box-info > .box-header {
background-color: #078e53;
}
'
))
),#tags$head
# SELECTION
# =========
conditionalPanel(condition = "input.sidebarmenu == 'selection'",
tags$i(class = "fa fa-check-square-o fa-5x"),
tags$h4(class = "titre", "Selection"),
helpText("Please choose a filter on the 'count' column [libs table] (0 for none) then select the groups. You can then update the samples and make your selection. Do not forget to update the selection and validate."),
tags$hr(),
tags$br(),
numericInput("nbFilterExtracted", "Filter (counts)", min = 0, value = 0, step = 1),
tags$br(),
fluidRow(
box(title = "Groups", solidHeader = T, collapsible = T, status = "primary",
checkboxInput('bar', 'All groups/None'),
uiOutput("libsGroup")
),#box Groups
box(title = "Samples", solidHeader = T, collapsible = T, status = "info",
actionButton("updateSamples", "Update samples"),
uiOutput("libsSamplename")
)
),#fluidRow
fluidRow(
column(1, actionButton("updateSelection", "Update selection")),
column(1, actionButton("updateCorMat", "Validate selection"))
)#fluidRow
),#conditionalPanel displayGroup
tabItems(
# HOME
# ====
tabItem(tabName = "home",
tags$i(class = "fa fa-home fa-5x"),
tags$h4(class = "titre", "General information"),
helpText("Need a description..."),
tags$hr(),
tags$div(id = "boxplotGeneral",
fluidRow(
column(2, selectInput("boxplotGroupsTotal", "Groups (total)", c("none", paste(unique(libs$group))), selected = "none")),
column(1, plotOutput("boxplotTotal"), height = "50px"),
column(1, plotOutput("boxplotSub"), height = "50px"),
column(2, uiOutput("UIboxplotGroupsSub"))
)#fluidRow
)#tags$div boxplot
),#tabItem home
# HIERARCHICAL CLUSTERING
# =======================
tabItem(tabName = "hierarchicalClustering",
# tags$i(class = "fa fa-sort-amount-desc fa-5x"),
# tags$h4(class = "titre", "Hierarchical clustering"),
# tags$hr(),
# DENDROGRAM
tabsetPanel(
tabPanel("Dendrogram",
tags$i(class = "fa fa-sitemap fa-5x"),
tags$h4(class = "titre", "Dendrogram"),
helpText("Need a description..."),
tags$hr(),
tags$div(class = "centeredInput",
numericInput("nbClusters", "Number of clusters", min = 1, max = nrow(libs), value = 1, step = 1)
),#tags$div centeredInput
tags$br(),
# fluidRow(
box(title = "Dendrogram", solidHeader = T, collapsible = T, status = "primary",
fluidRow(
column(3, sliderInput("dendroSize", "size", min = 0.1, max = 3, step = .1, value = 1)),
column(3, numericInput("heightDendro", "Plot height:", min = 20, max = 5000, value = 250)),
column(3, checkboxInput("dendroHoriz", "horizontal", FALSE)),
column(5, checkboxInput("showGroupsColor", "Color labels by group", FALSE))
),#fluidRow
actionButton("updateDendrogram", "Update dendrogram"),
tags$br(),
uiOutput("dendrogramPlot")
),#box Dendrogram
box(title = "Nodes height", solidHeader = T, collapsible = T, status = "warning",
sliderInput("heightlength", "Number of clusters to display", min = 2, max = 100, value = 10, step = 1),
actionButton("updateheight", "Update plot"),
plotOutput("height")
)#box Nodes height
# )#fluidRow
),#tabPanel Dendrogram
# HEATMAP
tabPanel("Heatmap",
tags$i(class = "fa fa-th-large fa-5x"),
tags$h4(class = "titre", "Heatmap"),
helpText("Need a description..."),
tags$hr(),
box(title = "Heatmap", solidHeader = T, collapsible = T, status = "primary", width = "950px", height = "950px",
fluidRow(column(1, actionButton("updateHeatmap", "Update heatmap"))),
tags$br(),
plotOutput("heatmapGenes", width = "950px", height = "750px")
)#box Heatmap
)#tabPanel Heatmap
)#tabsetPanel
),#tabItem hierarchicalClustering
# K-MEANS
# =======
tabItem(tabName = "k-means",
tags$i(class = "fa fa-dot-circle-o fa-5x"),
tags$h4(class = "titre", "K-means"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
tabsetPanel(
tabPanel("PCoA and k-means clustering",
helpText("1 - Perform PCoA first"),
helpText("2 - Check for optimal number of clusters"),
helpText("3 - Perform k-means"),
fluidRow(column(1, actionButton("updatePCoA", "Update PCoA"))),
fluidRow(
column(4, numericInput("kmeansClusters", h4("Number of clusters", style = "color: rgb(0, 123, 16)"), min = 1, max = nrow(libs), value = 1, step = 1)
)
),
fluidRow(column(1, actionButton("updatekmeans", "Update k-means"))),
plotOutput("pcoagenes12", brush = brushOpts(id = "pcoa12brush", resetOnNew = T), width = 470, dblclick = "pcoa12dblclick"),
dataTableOutput("dataPCoA"),
verbatimTextOutput("pcoasummary")
),
tabPanel("SSE",
helpText("Within sum of squared error plot."),
helpText("Needs to perform PCoA first."),
sliderInput("SSElength", "Number of clusters to display", min = 2, max = 100, value = 10, step = 1),
fluidRow(column(1, actionButton("updateSSE", "Update SSE plot"))),
plotOutput("SSE")
)
)
),#tabItem k-means
# PCA
# ===
tabItem(tabName = "PCA",
tags$i(class = "fa fa-area-chart fa-5x"),
tags$h4(class = "titre", "Principal Components Analysis"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
tabsetPanel(
tabPanel("Summary",
fluidRow(column(1, actionButton("updatePCASummary", "Update PCA"))),
plotOutput("eigenvalues"),
verbatimTextOutput("pcasummary")
),
tabPanel("Components",
fluidRow(column(1, actionButton("updatePCAComponents", "Update components"))),
fluidRow(
sliderInput("nbDispGenes", "How many genes:", min = 1, max = 100, value = 25, step = 1)
),
fluidRow(
plotOutput("components1"),
plotOutput("components2"),
plotOutput("components3")
)
),
tabPanel("Plots",
fluidRow(
column(2, radioButtons("PCAcolor", "Color points by:", choices = list("biological groups" = 1, "dendrogram clusters" = 2, "k-means clusters" = 3), selected = 1)),
column(2, checkboxInput("showEllipse", "Show ellipse(s)", value = F))
),
tabsetPanel(
tabPanel("2D",
fluidRow(column(1, actionButton("updatePCAPlots", "Update plots"))),
#
fluidRow(
column(6, plotOutput("interactPCA12li", brush = brushOpts(id = "PCA12librush", resetOnNew = T), width = 470, dblclick = "PCA12lidblclick"), align="center"),
column(6, plotOutput("interactPCA12co", brush = brushOpts(id = "PCA12cobrush", resetOnNew = T), width = 470, dblclick = "PCA12codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA12li")),
column(6, dataTableOutput("dataPCA12co"))
),
#
fluidRow(
column(6, plotOutput("interactPCA13li", brush = brushOpts(id = "PCA13librush", resetOnNew = T), width = 470, dblclick = "PCA13lidblclick"), align="center"),
column(6, plotOutput("interactPCA13co", brush = brushOpts(id = "PCA13cobrush", resetOnNew = T), width = 470, dblclick = "PCA13codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA13li")),
column(6, dataTableOutput("dataPCA13co"))
),
#
fluidRow(
column(6, plotOutput("interactPCA32li", brush = brushOpts(id = "PCA32librush", resetOnNew = T), width = 470, dblclick = "PCA32lidblclick"), align="center"),
column(6, plotOutput("interactPCA32co", brush = brushOpts(id = "PCA32cobrush", resetOnNew = T), width = 470, dblclick = "PCA32codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA32li")),
column(6, dataTableOutput("dataPCA32co"))
)
),#2D
tabPanel("3D",
sliderInput("pca3ddotsize", "Dot size: ", min = 1, max = 20, step = 1, value = 2),
actionButton("generatepca3d", "Update plot"),
plotly::plotlyOutput("pca3D", width = "950px", height = "750px")
)#3D
)#tabsetPanel Plots
),#tabsetPanel Plots
tabPanel("Checkplot",
fluidRow(column(2, radioButtons("dataCheckplot", "Dataset:", list("Total" = "total", "Selection" = "selection"), selected = "selection"))),
fluidRow(
column(2, textInput("geneNameCheckplot", label = "Gene name (or part of it):", value = "geneName...")),
column(1, actionButton("updatelistcheckplot", "Search gene"))
),
fluidRow(
column(2, uiOutput("geneNamescheckplotUI")),
column(2, uiOutput("sampleNamescheckplotUI"))
),
fluidRow(
column(2, numericInput("heightcheckplot", "Plot height", min = 250, max = 10000, value = 250)),
column(2, checkboxInput("facetcheckplot", label = "Separate by group", value = FALSE)),
column(1, actionButton("updatecheckplot", "Update checkplot"))
),
uiOutput("checkplotUI")
)
)
),#tabItems PCA
# CA
# ==
tabItem(tabName = "CA",
tags$i(class = "fa fa-area-chart fa-5x"),
tags$h4(class = "titre", "Correspondence Analysis"),
helpText("Need a description..."),
tags$hr(),
helpText("Needs to have generated PCA first."),
tabsetPanel(
tabPanel("Settings",
fluidRow(
selectInput("selectAxisCoA", h4("PCA Axis of interest on which we find the interesting genes", style = "color: rgb(0, 123, 16)"), choices = list("Axis 1" = 1, "Axis 2" = 2, "Axis 3" = 3), selected = 1)
),
fluidRow(column(1, actionButton("generateContributionBoxplot", "Generate contribution boxplot"))),
fluidRow(
plotOutput("contributionBoxplot", width = "950px", height = "200px")
),
fluidRow(
numericInput("nbGenesToKeep", h4("Threshold", style = "color: rgb(0, 123, 16)"), min = 0, max = 100, value = 20, step = 1)
),
fluidRow(column(1, actionButton("applyThreshold", "Apply threshold"))),
fluidRow(
column(3, verbatimTextOutput("numberGenesThresholded"))
),
fluidRow(
plotOutput("thresholdBoxplot", width = "950px", height = "200px")
),
fluidRow(
column(6, dataTableOutput("thresholdedPrint"))
)
),#Settings
tabPanel("Summary",
fluidRow(column(1, actionButton("updateCoA", "Update CoA"))),
plotOutput("coaeigenvalues"),
verbatimTextOutput("coasummary")
),#Summary
tabPanel("Plots",
radioButtons("COAcolor", "Color points by:", choices = list("biological groups" = 1, "dendrogram clusters" = 2, "k-means clusters" = 3), selected = 1),
tabsetPanel(
tabPanel("2D",
fluidRow(column(1, actionButton("updateCoAPlots", "Update plots"))),
tags$br(),
plotOutput("interactCOA12", brush = brushOpts(id = "COA12brush", resetOnNew = T), width = 470, dblclick = "COA12dblclick"),
dataTableOutput("dataCOA12"),
plotOutput("interactCOA13", brush = brushOpts(id = "COA13brush", resetOnNew = T), width = 470, dblclick = "COA13dblclick"),
dataTableOutput("dataCOA13"),
plotOutput("interactCOA32", brush = brushOpts(id = "COA32brush", resetOnNew = T), width = 470, dblclick = "COA32dblclick"),
dataTableOutput("dataCOA32")
),#2D
tabPanel("3D",
sliderInput("coa3ddotsize", "Dot size: ", min = 1, max = 20, step = 1, value = 2),
actionButton("generatecoa3d", "Update plot"),
plotly::plotlyOutput("coa3D", width = "950px", height = "750px")
)#3D
)#tabsetPanel
)#Plots
)#TabsetPanel
),#tabItems CA
# EXPORT
# ======
tabItem(tabName = "export",
tags$i(class = "fa fa-share-square-o fa-5x"),
tags$h4(class = "titre", "Export"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
helpText("Exportation is done in RDS format."),
fluidRow(
column(3, textInput("genesRDSName", label = h4("Genes : choose a name", style = "color: rgb(0, 16, 148)", align = "center"), value = paste0("genes_", gsub("\\s", "_", gsub("\\d{2}[:::]\\d{2}[:::]\\d{2}\\s", "", date())), ".rds")))
),
fluidRow(
column(1, actionButton("exportGenes", "Export 'genes' with current selection"))
),
fluidRow(
column(3, textInput("libsRDSName", label = h4("Libs : choose a name", style = "color: rgb(0, 16, 148)", align = "center"), value = paste0("libs_", gsub("\\s", "_", gsub("\\d{2}[:::]\\d{2}[:::]\\d{2}\\s", "", date())), ".rds")))
),
fluidRow(
column(1, actionButton("exportLibs", "Export 'libs' with current selection"))
)
),#tabItems export
# ABOUT
# =====
tabItem(tabName = "about",
tags$i(class = "fa fa-question-circle fa-5x"),
tags$h4(class = "titre", "About"),
helpText("Need a description..."),
tags$hr(),
helpText("Nothing for now...")
)#tabItems about
)#tabItems
)#dashboardBody
)#dashboardPage
shamansim/Vizection documentation built on May 29, 2019, 8:06 p.m.
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# loading required library
library(shiny)
library(shinydashboard)
library(ade4)
library(adegraphics)
library(magrittr)
library(dplyr)
library(ggplot2)
library(data.table)
library(colorspace)
library(smallCAGEqc)
library(dendextend)
library(DT)
if (getOption("vizection.dataIs") == "genesAndLibs") {
genes <- get(getOption("vizection.genes"), .GlobalEnv)
libs <- get(getOption("vizection.libs"), .GlobalEnv)
} else if (getOption("vizection.dataIs") == "se") {
se <- get(getOption("vizection.se"), .GlobalEnv)
genes <- SummarizedExperiment::assay(se) %>% data.frame
libs <- SummarizedExperiment::colData(se) %>% data.frame(stringsAsFactors = FALSE)
libs$samplename <- rownames(libs)
libs$counts <- colSums(genes)
} else stop("Could not detect what data to load.")
if (is.null(libs$group))
libs$group <- "No groups"
libs$group %<>% factor
# BEGIN shiny app
dashboardPage(
# HEADER
# ------
dashboardHeader(title = tags$img(src = "vizection_white.png", alt = "Vizection", height = "40"),
dropdownMenuOutput("tasksMenu")
),#dashboardHeader
# SIDEBAR
# -------
dashboardSidebar(#width = 450,
sidebarMenu(id = "sidebarmenu",
menuItem("Selection", tabName = "selection", icon = icon("check-square-o")),
menuItem("General", tabName = "home", icon = icon("home")),
menuItem("Clustering", tabName = "clustering", icon = icon("sort-amount-desc"),
menuSubItem("Hierarchical clustering", tabName = "hierarchicalClustering", icon = icon("sitemap")),
menuSubItem("K-means", tabName = "k-means", icon = icon("dot-circle-o"))
),#clustering
menuItem("PCA", tabName = "PCA", icon = icon("area-chart")
),#PCA
menuItem("CA", tabName = "CA", icon = icon("area-chart")
),#CA
menuItem("Export", tabName = "export", icon = icon("share-square-o")
),#Export
menuItem("About", tabName = "about", icon = icon("question-circle")
)#About
)#sidebarMenu
),#dashboardSidebar
# BODY
# ----
dashboardBody(
tags$head(
tags$style(HTML('
/*
body
*/
body {
height: auto;
overflow: auto;
font-family: sans-serif;
}
/*
header
*/
.skin-blue .main-header .logo {
background-color: black;
}
.skin-blue .main-header .navbar {
background-color: black;
}
/*
sidebar
*/
.skin-blue .left-side, .skin-blue .main-sidebar, .skin-blue .wrapper {
background-color: black;
}
.skin-blue .sidebar-menu > li.active > a, .skin-blue .sidebar-menu > li:hover > a {
background: black;
}
/*
content
*/
.content {
background-color: white;
}
.content-wrapper, .right-side {
background-color: white;
}
img {
display: block;
margin: auto;
}
.fa-5x {
text-align: center;
width: 100%;
}
.titre {
text-align: center;
width: 100%;
font-family: sans-serif;
font-variant: small-caps;
}
/* tabs */
.nav > li > a:active, .nav > li > a:focus, .nav > li > a:hover {
background: #ff9d00;
}
/* boxplotGeneral */
#boxplotGeneral {
text-align: center;
}
/* label above input (e.g. dendrogram nb clusters) */
.centeredInput {
text-align: center;
display: block;
margin: auto;
width: 10%;
}
/* solid boxes primary and info */
.box.box-solid.box-primary > .box-header {
background-color: #111987;
}
.box.box-solid.box-info > .box-header {
background-color: #078e53;
}
'
))
),#tags$head
# SELECTION
# =========
conditionalPanel(condition = "input.sidebarmenu == 'selection'",
tags$i(class = "fa fa-check-square-o fa-5x"),
tags$h4(class = "titre", "Selection"),
helpText("Please choose a filter on the 'count' column [libs table] (0 for none) then select the groups. You can then update the samples and make your selection. Do not forget to update the selection and validate."),
tags$hr(),
tags$br(),
numericInput("nbFilterExtracted", "Filter (counts)", min = 0, value = 0, step = 1),
tags$br(),
fluidRow(
box(title = "Groups", solidHeader = T, collapsible = T, status = "primary",
checkboxInput('bar', 'All groups/None'),
uiOutput("libsGroup")
),#box Groups
box(title = "Samples", solidHeader = T, collapsible = T, status = "info",
actionButton("updateSamples", "Update samples"),
uiOutput("libsSamplename")
)
),#fluidRow
fluidRow(
column(1, actionButton("updateSelection", "Update selection")),
column(1, actionButton("updateCorMat", "Validate selection"))
)#fluidRow
),#conditionalPanel displayGroup
tabItems(
# HOME
# ====
tabItem(tabName = "home",
tags$i(class = "fa fa-home fa-5x"),
tags$h4(class = "titre", "General information"),
helpText("Need a description..."),
tags$hr(),
tags$div(id = "boxplotGeneral",
fluidRow(
column(2, selectInput("boxplotGroupsTotal", "Groups (total)", c("none", paste(unique(libs$group))), selected = "none")),
column(1, plotOutput("boxplotTotal"), height = "50px"),
column(1, plotOutput("boxplotSub"), height = "50px"),
column(2, uiOutput("UIboxplotGroupsSub"))
)#fluidRow
)#tags$div boxplot
),#tabItem home
# HIERARCHICAL CLUSTERING
# =======================
tabItem(tabName = "hierarchicalClustering",
# tags$i(class = "fa fa-sort-amount-desc fa-5x"),
# tags$h4(class = "titre", "Hierarchical clustering"),
# tags$hr(),
# DENDROGRAM
tabsetPanel(
tabPanel("Dendrogram",
tags$i(class = "fa fa-sitemap fa-5x"),
tags$h4(class = "titre", "Dendrogram"),
helpText("Need a description..."),
tags$hr(),
tags$div(class = "centeredInput",
numericInput("nbClusters", "Number of clusters", min = 1, max = nrow(libs), value = 1, step = 1)
),#tags$div centeredInput
tags$br(),
# fluidRow(
box(title = "Dendrogram", solidHeader = T, collapsible = T, status = "primary",
fluidRow(
column(3, sliderInput("dendroSize", "size", min = 0.1, max = 3, step = .1, value = 1)),
column(3, numericInput("heightDendro", "Plot height:", min = 20, max = 5000, value = 250)),
column(3, checkboxInput("dendroHoriz", "horizontal", FALSE)),
column(5, checkboxInput("showGroupsColor", "Color labels by group", FALSE))
),#fluidRow
actionButton("updateDendrogram", "Update dendrogram"),
tags$br(),
uiOutput("dendrogramPlot")
),#box Dendrogram
box(title = "Nodes height", solidHeader = T, collapsible = T, status = "warning",
sliderInput("heightlength", "Number of clusters to display", min = 2, max = 100, value = 10, step = 1),
actionButton("updateheight", "Update plot"),
plotOutput("height")
)#box Nodes height
# )#fluidRow
),#tabPanel Dendrogram
# HEATMAP
tabPanel("Heatmap",
tags$i(class = "fa fa-th-large fa-5x"),
tags$h4(class = "titre", "Heatmap"),
helpText("Need a description..."),
tags$hr(),
box(title = "Heatmap", solidHeader = T, collapsible = T, status = "primary", width = "950px", height = "950px",
fluidRow(column(1, actionButton("updateHeatmap", "Update heatmap"))),
tags$br(),
plotOutput("heatmapGenes", width = "950px", height = "750px")
)#box Heatmap
)#tabPanel Heatmap
)#tabsetPanel
),#tabItem hierarchicalClustering
# K-MEANS
# =======
tabItem(tabName = "k-means",
tags$i(class = "fa fa-dot-circle-o fa-5x"),
tags$h4(class = "titre", "K-means"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
tabsetPanel(
tabPanel("PCoA and k-means clustering",
helpText("1 - Perform PCoA first"),
helpText("2 - Check for optimal number of clusters"),
helpText("3 - Perform k-means"),
fluidRow(column(1, actionButton("updatePCoA", "Update PCoA"))),
fluidRow(
column(4, numericInput("kmeansClusters", h4("Number of clusters", style = "color: rgb(0, 123, 16)"), min = 1, max = nrow(libs), value = 1, step = 1)
)
),
fluidRow(column(1, actionButton("updatekmeans", "Update k-means"))),
plotOutput("pcoagenes12", brush = brushOpts(id = "pcoa12brush", resetOnNew = T), width = 470, dblclick = "pcoa12dblclick"),
dataTableOutput("dataPCoA"),
verbatimTextOutput("pcoasummary")
),
tabPanel("SSE",
helpText("Within sum of squared error plot."),
helpText("Needs to perform PCoA first."),
sliderInput("SSElength", "Number of clusters to display", min = 2, max = 100, value = 10, step = 1),
fluidRow(column(1, actionButton("updateSSE", "Update SSE plot"))),
plotOutput("SSE")
)
)
),#tabItem k-means
# PCA
# ===
tabItem(tabName = "PCA",
tags$i(class = "fa fa-area-chart fa-5x"),
tags$h4(class = "titre", "Principal Components Analysis"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
tabsetPanel(
tabPanel("Summary",
fluidRow(column(1, actionButton("updatePCASummary", "Update PCA"))),
plotOutput("eigenvalues"),
verbatimTextOutput("pcasummary")
),
tabPanel("Components",
fluidRow(column(1, actionButton("updatePCAComponents", "Update components"))),
fluidRow(
sliderInput("nbDispGenes", "How many genes:", min = 1, max = 100, value = 25, step = 1)
),
fluidRow(
plotOutput("components1"),
plotOutput("components2"),
plotOutput("components3")
)
),
tabPanel("Plots",
fluidRow(
column(2, radioButtons("PCAcolor", "Color points by:", choices = list("biological groups" = 1, "dendrogram clusters" = 2, "k-means clusters" = 3), selected = 1)),
column(2, checkboxInput("showEllipse", "Show ellipse(s)", value = F))
),
tabsetPanel(
tabPanel("2D",
fluidRow(column(1, actionButton("updatePCAPlots", "Update plots"))),
#
fluidRow(
column(6, plotOutput("interactPCA12li", brush = brushOpts(id = "PCA12librush", resetOnNew = T), width = 470, dblclick = "PCA12lidblclick"), align="center"),
column(6, plotOutput("interactPCA12co", brush = brushOpts(id = "PCA12cobrush", resetOnNew = T), width = 470, dblclick = "PCA12codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA12li")),
column(6, dataTableOutput("dataPCA12co"))
),
#
fluidRow(
column(6, plotOutput("interactPCA13li", brush = brushOpts(id = "PCA13librush", resetOnNew = T), width = 470, dblclick = "PCA13lidblclick"), align="center"),
column(6, plotOutput("interactPCA13co", brush = brushOpts(id = "PCA13cobrush", resetOnNew = T), width = 470, dblclick = "PCA13codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA13li")),
column(6, dataTableOutput("dataPCA13co"))
),
#
fluidRow(
column(6, plotOutput("interactPCA32li", brush = brushOpts(id = "PCA32librush", resetOnNew = T), width = 470, dblclick = "PCA32lidblclick"), align="center"),
column(6, plotOutput("interactPCA32co", brush = brushOpts(id = "PCA32cobrush", resetOnNew = T), width = 470, dblclick = "PCA32codblclick"), align="center")
),
fluidRow(
column(6, dataTableOutput("dataPCA32li")),
column(6, dataTableOutput("dataPCA32co"))
)
),#2D
tabPanel("3D",
sliderInput("pca3ddotsize", "Dot size: ", min = 1, max = 20, step = 1, value = 2),
actionButton("generatepca3d", "Update plot"),
plotly::plotlyOutput("pca3D", width = "950px", height = "750px")
)#3D
)#tabsetPanel Plots
),#tabsetPanel Plots
tabPanel("Checkplot",
fluidRow(column(2, radioButtons("dataCheckplot", "Dataset:", list("Total" = "total", "Selection" = "selection"), selected = "selection"))),
fluidRow(
column(2, textInput("geneNameCheckplot", label = "Gene name (or part of it):", value = "geneName...")),
column(1, actionButton("updatelistcheckplot", "Search gene"))
),
fluidRow(
column(2, uiOutput("geneNamescheckplotUI")),
column(2, uiOutput("sampleNamescheckplotUI"))
),
fluidRow(
column(2, numericInput("heightcheckplot", "Plot height", min = 250, max = 10000, value = 250)),
column(2, checkboxInput("facetcheckplot", label = "Separate by group", value = FALSE)),
column(1, actionButton("updatecheckplot", "Update checkplot"))
),
uiOutput("checkplotUI")
)
)
),#tabItems PCA
# CA
# ==
tabItem(tabName = "CA",
tags$i(class = "fa fa-area-chart fa-5x"),
tags$h4(class = "titre", "Correspondence Analysis"),
helpText("Need a description..."),
tags$hr(),
helpText("Needs to have generated PCA first."),
tabsetPanel(
tabPanel("Settings",
fluidRow(
selectInput("selectAxisCoA", h4("PCA Axis of interest on which we find the interesting genes", style = "color: rgb(0, 123, 16)"), choices = list("Axis 1" = 1, "Axis 2" = 2, "Axis 3" = 3), selected = 1)
),
fluidRow(column(1, actionButton("generateContributionBoxplot", "Generate contribution boxplot"))),
fluidRow(
plotOutput("contributionBoxplot", width = "950px", height = "200px")
),
fluidRow(
numericInput("nbGenesToKeep", h4("Threshold", style = "color: rgb(0, 123, 16)"), min = 0, max = 100, value = 20, step = 1)
),
fluidRow(column(1, actionButton("applyThreshold", "Apply threshold"))),
fluidRow(
column(3, verbatimTextOutput("numberGenesThresholded"))
),
fluidRow(
plotOutput("thresholdBoxplot", width = "950px", height = "200px")
),
fluidRow(
column(6, dataTableOutput("thresholdedPrint"))
)
),#Settings
tabPanel("Summary",
fluidRow(column(1, actionButton("updateCoA", "Update CoA"))),
plotOutput("coaeigenvalues"),
verbatimTextOutput("coasummary")
),#Summary
tabPanel("Plots",
radioButtons("COAcolor", "Color points by:", choices = list("biological groups" = 1, "dendrogram clusters" = 2, "k-means clusters" = 3), selected = 1),
tabsetPanel(
tabPanel("2D",
fluidRow(column(1, actionButton("updateCoAPlots", "Update plots"))),
tags$br(),
plotOutput("interactCOA12", brush = brushOpts(id = "COA12brush", resetOnNew = T), width = 470, dblclick = "COA12dblclick"),
dataTableOutput("dataCOA12"),
plotOutput("interactCOA13", brush = brushOpts(id = "COA13brush", resetOnNew = T), width = 470, dblclick = "COA13dblclick"),
dataTableOutput("dataCOA13"),
plotOutput("interactCOA32", brush = brushOpts(id = "COA32brush", resetOnNew = T), width = 470, dblclick = "COA32dblclick"),
dataTableOutput("dataCOA32")
),#2D
tabPanel("3D",
sliderInput("coa3ddotsize", "Dot size: ", min = 1, max = 20, step = 1, value = 2),
actionButton("generatecoa3d", "Update plot"),
plotly::plotlyOutput("coa3D", width = "950px", height = "750px")
)#3D
)#tabsetPanel
)#Plots
)#TabsetPanel
),#tabItems CA
# EXPORT
# ======
tabItem(tabName = "export",
tags$i(class = "fa fa-share-square-o fa-5x"),
tags$h4(class = "titre", "Export"),
helpText("Need a description..."),
tags$hr(),
helpText("Do not forget to 'validate parameters'."),
helpText("Exportation is done in RDS format."),
fluidRow(
column(3, textInput("genesRDSName", label = h4("Genes : choose a name", style = "color: rgb(0, 16, 148)", align = "center"), value = paste0("genes_", gsub("\\s", "_", gsub("\\d{2}[:::]\\d{2}[:::]\\d{2}\\s", "", date())), ".rds")))
),
fluidRow(
column(1, actionButton("exportGenes", "Export 'genes' with current selection"))
),
fluidRow(
column(3, textInput("libsRDSName", label = h4("Libs : choose a name", style = "color: rgb(0, 16, 148)", align = "center"), value = paste0("libs_", gsub("\\s", "_", gsub("\\d{2}[:::]\\d{2}[:::]\\d{2}\\s", "", date())), ".rds")))
),
fluidRow(
column(1, actionButton("exportLibs", "Export 'libs' with current selection"))
)
),#tabItems export
# ABOUT
# =====
tabItem(tabName = "about",
tags$i(class = "fa fa-question-circle fa-5x"),
tags$h4(class = "titre", "About"),
helpText("Need a description..."),
tags$hr(),
helpText("Nothing for now...")
)#tabItems about
)#tabItems
)#dashboardBody
)#dashboardPage
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