inst/sake/ui.R
In naikai/sake: Single-cell RNA-Seq Analysis and Klustering Evaluation
library(shiny)
library(shinyBS)
library(plotly)
library(d3heatmap)
library(htmltools)
library(shinythemes)
library(shinydashboard)
library(DT)
tg <- tags$div()
tg <- attachDependencies(
tg,
htmlDependency(
name = "plotlyjs",
version = "1.13.0",
src = c(href = "https://cdn.plot.ly"),
script = "plotly-latest.min.js"
)
)
header <- dashboardHeader(
title = "SAKE"
)
sidebar <- dashboardSidebar(
sidebarMenu(id = "sidebarmenu",
menuItem("File", tabName="File", icon=icon('file-text-o')),
menuItem("Quality control", tabName="quality", icon = icon('bar-chart-o')),
menuItem("Filtering", tabName="filter", icon = icon('scissors'),
menuSubItem("Identify Variable Genes", tabName="featureSelection"),
menuSubItem("Sample Scatter Plot", tabName="scatter")
),
menuItem("NMF", tabName="NMF", icon = icon('sitemap'),
menuSubItem("NMF Run", tabName="nmfRun"),
menuSubItem("Identify Groups", tabName="nmfGroups"),
menuSubItem("Extract features", tabName="nmfFeatures")
),
menuItem("Visualization", tabName="Visualization", icon = icon('calendar', lib = "glyphicon")
),
conditionalPanel("input.sidebarmenu === 'Visualization'",
selectInput("VisType",
label = "What kind of plot?",
choices = c("Heatmap", "PCA", "t-SNE", "Cluster Distribution"),
selected = "Cluster Distribution")
),
menuItem("Differential analysis", tabName="DE", icon = icon('tasks', lib="glyphicon"),
menuSubItem("DESeq2", tabName="DESeq2")
# menuSubItem("SCDE", tabName="SCDE")
),
menuItem("Enrichment analysis", tabName="enrichment", icon = icon('cogs')
),
conditionalPanel("input.sidebarmenu === 'enrichment'",
selectInput("EnrichType",
label = "Which Category?",
choices = c("GO", "KEGG"),
selected = "KEGG")
)
)
)
body <- dashboardBody(
tags$head(tags$script(src = "google-analytics.js")),
tags$head(tags$style(".dwnld{background-color:#8E24BF;} .dwnld{color: white;} .dwnld{border-color:$9932CC;}")),
tags$head(tags$style(".act{background-color:#337AB7;} .act{color: #FFFFFF;} .act{border-color:$2E6DA4;}")),
tags$head(tags$style(".stop{background-color:#4F1E0A;} .stop{color: #FFFFFF;} .stop{border-color:#612C0E;}")),
tags$head(tg),
tabItems(
tabItem("File",
fluidRow(
tabBox( width=5, id = "tabset1", height = "575px",
tabPanel("Upload file",
selectInput("selectfile",
label = "How do you want to upload the data?",
choices = list("Select from preloaded data"='preload',
"Upload rawdata"='upload',
"Upload saved NMF run"='saved'),
selected = "saved"),
conditionalPanel(
condition = "input.selectfile == 'upload'",
fileInput('file1', 'Choose text File',
accept=c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv',
'.tsv',
'.out'))
),
conditionalPanel(
condition = "input.selectfile == 'preload'",
selectizeInput(inputId = 'inputdata',
label = 'Choose DataSet',
choices = filenames,
options = list(
placeholder = 'Please select a data set',
onInitialize = I('function() { this.setValue(""); }')
)
)
),
conditionalPanel(
condition = "input.selectfile == 'upload' || input.selectfile == 'preload'",
tags$hr(),
checkboxInput('header', 'Header', TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
selected='\t')
),
conditionalPanel(
condition = "input.selectfile == 'saved'",
fileInput('rda', 'Choose saved RData',
accept=c('.rda',
'.RData'))
)
),
tabPanel("System setup",
selectInput("ncores", label = "Specify number of cores to run analysis",
choices = c(1,2,4,8,16,24,32,48,64),
selected = 16)
)
),
column(width = 7,
box(width = NULL, title="Preview Raw Data", status = "warning",
DT::dataTableOutput('rawdatatbl', height = "439px"),
tags$hr(),
p(
class = "text-muted",
paste("Note: You can preview the loaded data, ",
"make sure the format is what you want to then proceed."
)
)
)
)
),
fluidRow(
infoBoxOutput("nameBox"),
infoBoxOutput("sampleBox"),
infoBoxOutput("featureBox")
)
),
tabItem("quality",
fluidRow(
box(title="Datatable", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=3,
selectInput("normdata", label = "Normalization",
choices = list("RPM normalization" = "takeRPM",
"DESeq size factor normalization" = "takesizeNorm",
"Upper Quartile normalization" = "takeuq",
"None" = "none"),
selected = "none")
),
column(width=3,
selectInput("transformdata", label = "Transformation",
choices = list("Variance Stablizing Transformation" = "takevst",
"Log transformation" = "takelog",
"None" = "none"),
selected = "none")
),
column(width=2, br(),
actionButton("run_transf", "Run Transform", icon("play-circle"), class = 'act')
),
column(width=2, br(),
downloadButton("dl_transformdata", "Download Data", class="dwnld")
)
)
)
),
fluidRow(
column(width=6,
box(title="Read Distribution", width=NULL, solidHeader=TRUE, status="info", height="570px",
plotlyOutput("readDistrib", height=500)
)
),
column(width=6,
box(title="Samples", width=NULL, solidHeader=TRUE, status="info", height="350px",
DT::dataTableOutput("selsamp_tb")
),
box(title="Filter", width=NULL, solidHeader=TRUE, status="info", height="200px",
verbatimTextOutput("selsamp_txt"),
actionButton("run_filtsamp", "Submit", icon("play-circle"), class = 'act')
)
)
)
),
tabItem("featureSelection",
fluidRow(
box(width=12, collapsible = TRUE,
fluidRow(
column(width=2,
selectInput("list_type",
label = "How to extract genes",
choices = c("Whole transcriptome",
"Top Ranks",
"Upload gene list"),
selected = "Top Ranks"
)
),
conditionalPanel(
condition = "input.list_type == 'Top Ranks'",
column(width=2, selectInput("orders",
label = "Rank order by",
choices = c("top", "bottom"),
selected = "top")),
column(width=2, selectInput("math",
label = "Arithmetic",
choices = c("mean", "median", "mad", "var"),
selected = "mad")),
column(width=4, sliderInput("top.num",
label = "How many genes to select", ticks = FALSE,
min=500, max=20000, value=5000, step = 500)
)
),
conditionalPanel(
condition = "input.list_type == 'Upload gene list'",
column(width=3,
fileInput('genefile', 'Choose gene list file',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv'))
)
)
)
)
),
fluidRow(
column(width=6,
featureUI("sample", title = "Sample Correlation")
),
column(width=6,
networkUI("gene", title = "Gene Network")
)
)
),
tabItem("scatter",
fluidRow(
box(title="Parameters", width=12, solidHeader = TRUE, status="success",
fluidRow(
column(width=2, selectizeInput("cor_sample1", label = "Sample on X-axis", choices=NULL, multiple=FALSE)),
column(width=2, selectizeInput("cor_sample2", label = "Sample on Y-axis", choices=NULL, multiple=FALSE)),
column(width=2, checkboxInput("scatter_log", label = "Log transformation", TRUE)),
column(width=2, checkboxInput("show_r2", label = "Show R2", FALSE)),
column(width=2, checkboxInput("show_fc", label = "Show Fold-Change line", TRUE))
)
),
box(width=6, height="520px", plotlyOutput('scatterPlot')),
box(width=6, height="520px", DT::dataTableOutput('scatterdatatbl'))
)
),
tabItem("nmfRun",
fluidRow(
box(title="Estimate Number of Clusters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2,
selectInput("mode",
label = "Which module?",
choices = c("Estimate number of k" = "estim",
"Real run" = "real"),
selected = "real"),
bsTooltip("mode", "We recommend you run estimate number of cluster (k) first <br> Then use the best estimate cluster (k) for real run",
"right", options = list(container = "body"))
),
column(width=2,
numericInput("num_cluster",
label = "Estimate clusters",
min=2, max=10, value=3, step=1),
bsTooltip("num_cluster", "For Estimate number of k, it will run NMF from 2 to k clusters <br><br> For Real run, it will run NMF for that specific k cluster",
"right", options = list(container = "body"))
),
column(width=2,
numericInput("nrun",
label = "Num of runs",
min=10, max=200, value=7, step=10),
bsTooltip("nrun", "Recommend 20-30 for estimate k <br> 50-100 for real run",
"right", options = list(container = "body"))
),
column(width=2,
actionButton("runNMF", "Run NMF", icon("play-circle"), class = 'act'),
checkboxInput('nmf_runmoreopt', 'More Options', FALSE)
),
uiOutput("dlNMF_UI"),
column(width=6, bsAlert("NMFAlert")),
column(width=6, bsAlert("YabiAlert")),
bsModal("nmfModal1", "Warning!\nYour gene size is above 1000, it will take longer than usual.\nAre you sure you want to continue?",
trigger = "", size = "small",
textInput("mailto", label = "To:", placeholder = "To:"),
actionButton("nmf_yabi_yes", 'Yes'),
actionButton("nmf_yabi_no", 'No')
)
),
fluidRow(
conditionalPanel(
condition = "input.nmf_runmoreopt == true",
column(width=2,
selectInput("algorithm",
label = "NMF algorithm",
choices = c("brunet", "lee", "nsNMF", "KL", "Frobenius"),
selected = "brunet"),
bsTooltip("algorithm", "Different NMF algorithms",
"right", options = list(container = "body"))
),
column(width=2,
selectInput("nmf_seed",
label = "Random seed",
choices = c("Yes"=0, "No"=123211),
selected = 123211),
bsTooltip("nmf_seed", "Whether to use random starting condition for NMF run <br> Default: fixed seed",
"right", options = list(container = "body"))
)
)
)
)
),
conditionalPanel(
condition = "input.mode == 'estim'",
fluidRow(
box(title="NMF Plot", width=8, solidHeader = TRUE, status="info", height="1000px",
collapsible = TRUE,
fluidRow(
column(width=4, selectInput("estimtype",
label = "What kind of plot?",
choices = c("Quality measures" = "quality",
"Consensus matrics" = "consensus"),
selected = "consensus")
),
column(width=3, br(),
downloadButton("dl_nmf_estimplot", "Save plot", class="dwnld")
),
fluidRow(
column(width=4,
br(),
checkboxInput('dl_nmf_estimmoreopt', 'More Options', FALSE),
conditionalPanel(
condition = "input.dl_nmf_estimmoreopt == true",
strong("PDF size (inches):"),
sliderInput(inputId="estim_pdf_w", label = "width:", min=3, max=20, value=14, ticks=F),
sliderInput(inputId="estim_pdf_h", label = "height:", min=3, max=20, value=14, ticks=F)
)
)
)
),
fluidRow(
column(width=12,
plotOutput('estimPlot')
)
)
),
box(title="Stats", width=4, solidHeader = TRUE, status="warning", height="1000px",
DT::dataTableOutput('estimSummary')
)
)
),
conditionalPanel(
condition = "input.mode == 'real'",
fluidRow(
box(title="NMF Plot", width=8, solidHeader = TRUE, status="info", height = "1000px",
fluidRow(
column(width=4, selectInput("plottype",
label = "What kind of plot?",
choices = c("samples", "features", "consensus"),
selected = "samples")
),
column(width=3, br(),
downloadButton("dl_nmf_realplot", "Save plot", class="dwnld")
),
fluidRow(
column(width=4,
br(),
checkboxInput('dl_nmf_realmoreopt', 'More Options', FALSE),
conditionalPanel(
condition = "input.dl_nmf_realmoreopt == true",
checkboxInput('nmfplot_silhouette', 'Match silhouette order', TRUE),
strong("PDF size (inches):"),
sliderInput(inputId="real_pdf_w", label = "width:", min=3, max=20, value=14, ticks=F),
sliderInput(inputId="real_pdf_h", label = "height:", min=3, max=20, value=14, ticks=F)
)
)
)
),
fluidRow(
column(width=12, plotOutput('nmfplot'))
)
),
box(title="Silhouette Plot", width=4, solidHeader=TRUE, status="info", height="1000px",
plotOutput('silhouetteplot')
)
)
)
),
tabItem("nmfGroups",
fluidRow(
box(title="Identify Groups", width=4, solidHeader=TRUE, status="success", height = "740px",
fluidRow(
column(width=12,
selectInput("predict",
label = "How to assign NMF groups?",
choices = c("consensus", "samples"),
selected = "samples"),
checkboxInput('nmf_prob', 'Change dot size based on probability?', FALSE),
bsTooltip("nmf_prob", "Samples with higher uncertainty (lower probability of being correctly assigned) will become larger dots",
"right", options = list(container = "body")),
DT::dataTableOutput('nmfGroups')
)
)
),
tabBox(title=tagList(shiny::icon("th-large"), "t-SNE Plot"),
width=8, id = "tabset2", side = "right", height = "740px",
tabPanel("2D",
column(width=2, numericInput("nmftsne_perplexity", label = "Perplexity", value=10)),
column(width=2, br(),
actionButton("run_nmftSNE", "Run t-SNE!", icon("play-circle"), class = "act")
),
uiOutput("dlperplexity_UI"),
column(width=6, bsAlert("perplexityAlert")),
column(width=12,
plotlyOutput('nmftsneplot', height=600, width=600)
)
)
)
)
),
tabItem("nmfFeatures",
fluidRow(
box(title="Extract Features", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=3,
selectInput("select_method",
label = "Feature selection method",
choices = c("Total"="total", "By default"="default", "rank"="rank"),
selected = "default")
),
conditionalPanel(
condition = "input.select_method == 'default' | input.select_method == 'rank'",
column(width=3,
selectInput("select_feature_num",
label = "Number of features from each group",
choices = c("By default"=0, "10"=10, "20"=20, "30"=30, "50"=50, "100"=100, "200"=200, "500"=500, "1000"=1000, "1500"=1500, "2000"=2000, "3000"=3000, "5000"=5000),
selected = 0)
),
conditionalPanel(
condition = "input.select_method == 'rank'",
column(width=3,
selectInput("select_math",
label = "Rank based on:",
choices = c("mean", "median", "mad", "var"),
selected = "mad")
),
column(width=3,
numericInput("select_FScutoff",
label = "FeatureScore cutoff",
min=0.5, max=1, value=0.86, step = 0.01)
)
)
),
column(width=1, selectInput("sel_vioplot",
label = "Plot type",
choices = c("box", "violin"),
selected = "violin")
),
column(width=1, selectInput("sel_vioscale",
label = "Y-scale",
choices = c("Raw", "log2", "log10"),
selected = "log2")
),
checkboxInput('nmf_feature_moreopt', 'More Options', FALSE)
),
conditionalPanel(
condition = "input.nmf_feature_moreopt == true",
fluidRow(
column(width=2, selectInput("sel_grp",
label = "Order:",
choices = c("Default", "Manual"),
selected = "Default")
),
conditionalPanel(
condition = "input.sel_grp == 'Manual'",
column(width=2, selectizeInput("sel_grp_order",
label="Select group order",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
column(width=1, numericInput("sel_xfont_size",
label = "Xlab size",
min=1, max=20, value=11, step=1)
),
column(width=1, numericInput("sel_yfont_size",
label = "Ylab size",
min=1, max=20, value=12, step=1)
),
column(width=1, numericInput("sel_ncol",
label = "# Column",
min=1, max=10, value=1, step=1)
),
column(width=1, numericInput("sel_ymax",
label = "Ymax",
min=0, max=1000000, value=0, step=1)
),
column(width=1, numericInput("sel_ymin",
label = "Ymin",
min=0, max=1000000, value=0, step=1)
),
column(width=1, numericInput("sel_title_size",
label = "Title size",
min=1, max=20, value=14, step=1)
),
column(width=2, numericInput("sel_legend_size",
label = "Legend size",
min=1, max=20, value=9, step=1)
),
column(width=1, checkboxInput("sel_show_legend", label="Show Legend", TRUE))
)
),
fluidRow(
column(width=7, DT::dataTableOutput('nmfFeatures')),
column(width=5, plotlyOutput('nmf_vioplot', height = "500px"))
# column(width=5, plotlyOutput('nmf_vioplot'))
)
)
)
),
tabItem("Visualization",
fluidRow(
box(width=12,
fluidRow(
column(width=3, selectInput("heatmapGene",
label = "How to select genes?",
choices = c("From NMF features" = "nmf",
"Ranks from data" = "rank",
"Upload gene list" = "upload",
"Manually select genes" = "manual",
"Preloaded gene list" = "preload"),
selected = "rank")
),
conditionalPanel(
condition = "input.heatmapGene == 'upload'",
column(width=3, fileInput('heatmapGeneFile', 'Choose text File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv',
'.out'))
)
),
conditionalPanel(
condition = "input.heatmapGene == 'rank'",
column(width=3, selectInput("heat.math",
label = "Rank by which metrics?",
choices = c("Mean Expression" = "mean",
"Median Expression" = "median",
"Median Absolute Deviation" = "mad",
"Variance" = "var"),
selected = "mad")
),
column(width=2, selectInput("heat.top.num",
label = "How many top genes:",
choices = c(seq(10,100,10), seq(200, 1000, 100), seq(2000, 15000, 1000)),
selected = 5000)
)
),
conditionalPanel(
condition = "input.heatmapGene == 'preload'",
column(width=3,
selectizeInput("predefined_list",
label = "Select from predefined lists",
choices = list(
Melanoma = c(`Keith Hoek (105 genes, 2006, Pigment Cell & Melanoma)`='Hoek',
`Widmer (Hoek's group) (97 genes, 2012, Pigment Cell & Melanoma)`='Widmer',
`Stein Aert (core 59 genes, 2015, Nature Communication)`='Stein_core',
`Stein Aert (whole 1412 genes, 2015, Nature Communication)`='Stein',
`SKCM NMF-K3 (53 genes, 2015, TCGA)`='SKCM-NMF-K3',
`SKCM NMF-K3-VST (101 genes, 2015, TCGA)`='SKCM-NMF-K3-VST-1000nrun',
`SKCM NMF-K4 (61 genes, 2015, TCGA)`='SKCM-NMF-K4',
`Stephanie Kreis (20 miRNAs, 2015, Oncotarget)`='Melanoma-20miRNA'
),
Hallmark = c(`Cell Cycle phases`='CellCycle',
`Regulation of Cell cycle`='RegCC',
`EMT`='EMT',
`KRAS`='KRAS',
`WNT`='WNT'
),
Others = c(`Resistance markers (combined from literature search)`='PubResistList',
`Resistance markers (Only select core)`='PubResistList_core',
`My 4 groups Gene List (May modify later)`='MyFourGroups',
`Aviv Regev cell cycle genes`='Aviv'
),
Mouse = c(`Cell Cycle phases`='Mouse_CellCycle',
`Regulation of Cell cycle`='Mouse_RegCellCycle'
),
Breast = c(`PAM50`='PAM50',
`BRCA-NMF-K5`="BRCA-NMF-K5"
)
),
selected = 'EMT', multiple=TRUE
)
)
),
conditionalPanel(
condition = "input.heatmapGene == 'manual'",
column(width=3, selectizeInput("manual_genes",
label = "Select your favorite genes",
choices=NULL, multiple=TRUE,
options = list(placeholder='Type in gene name, e.g. MYC, ERBB2')
)
)
),
conditionalPanel(
condition = "input.VisType == 'Heatmap' ",
column(width = 3, br(),
downloadButton("dl_heatmap", "Download", class = "butt"),
tags$head(tags$style(".butt{background-color:#8E24BF;} .butt{color: white;} .butt{border-color:$9932CC;}"))
)
),
column(width=2, checkboxInput('heatmoreopt', 'More Options', FALSE)),
conditionalPanel(
condition = "input.VisType == 't-SNE' ",
column(width=2, actionButton("runtSNE", "Run t-SNE!", icon("play-circle"), width="100px", class = 'act'))
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & input.VisType == 'Heatmap' ",
fluidRow(
column(width=6,
selectInput("heat_opttype",
label = "Which paramter type",
choices = list('Figure adjustment' = c(`Column Side Colors` = 'ColSideColors',
`Row Side Colors` = 'RowSideColors',
`Heatmap Color Scheme` = 'heatcolor',
`PDF Size` = 'pdf',
`Label Size and Apperance` = 'labels'),
'Clustering' = c(`Cluster Row or Column` = 'cluster',
`How to Scale Data` = 'scale',
`Distance and Linkage` = 'distnlink')),
selected = "cluster")
)
),
conditionalPanel(
condition = "input.heat_opttype == 'cluster'",
fluidRow(
column(width=3,
selectizeInput("OrdCol",
label = "Cluster column?",
choices=NULL, multiple=FALSE,
options = list(placeholder='Hierarchical')),
conditionalPanel(
condition = "input.OrdCol == 'Filename'",
numericInput("sortcolumn_num", label = "Sort by which group:", value = 1, min = 1)
),
conditionalPanel(
condition = "input.OrdCol == 'GeneExpr'",
selectizeInput("sortgene_by",
label = "Select a gene to sort on",
choices=NULL, multiple=FALSE,
options = list(placeholder='Type in gene name, e.g. MYC, ERBB2'))
)
),
column(width=3, selectInput("OrdRow", "Cluster row?",
choices = list("Default ordering" = "default",
"Hierarchical" = "hier"),
selected = "hier"))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'scale'",
fluidRow(
column(width=3, selectInput("scale_method", label = "How to scale data:", choices = c("mean", "median"), selected = "mean")),
column(width=4, checkboxInput("scale_first", "Scale data before dendrogram?", FALSE))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'distnlink'",
fluidRow(
column(width=3, selectInput("linkage",
label = "Which linkage methods?",
choices = c("average", "centroid", "complete","mcquitty", "median", "ward.D", "ward.D2", "single"),
selected = "ward.D2")),
column(width=3, selectInput("distance",
label = "Which distance methods?",
choices = c("binary", "canberra","euclidean", "manhattan", "maximum", "minkowski"),
selected = "euclidean"))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'labels'",
fluidRow(
column(width=2, textInput("title", label= "Figure title", value=NULL)),
column(width=2, numericInput("cexRow", label = "Row LabelSize:", value=0.5, step=0.1)),
column(width=2, numericInput("cexCol", label = "Col LabelSize:", value=0.4, step=0.1)),
column(width=3, selectInput('row_legend', label='Show legend for RowSide Colors?',
choices = c("Yes" = TRUE, "No" = FALSE))),
column(width=3, selectInput('col_legend', label='Show legend for ColumnSide Colors?',
choices = c("Yes" = TRUE, "No" = FALSE)))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'heatcolor'",
fluidRow(
column(width=3, selectInput("heatColorScheme",
label = "Heat map color scheme",
choices = c("RdBu_Brewer", "RdYlBu_Brewer", "Red_Green", "Red_Blue","Rainbow", "Black_White"),
selected = "RdBu_Brewer")),
column(width=3, numericInput("color_num", label = "Num", value = 9, min=5))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'pdf'",
fluidRow(
column(width=3, sliderInput("heat_pdf_wd", label = "Width (inch)", value=14, min=5, max=20, ticks=F)),
column(width=3, sliderInput("heat_pdf_ht", label = "Height (inch)", value=12, min=5, max=20, ticks=F))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'RowSideColors'",
fluidRow(
column(width=3, selectInput("RowColScheme",
label = "Row Color Scheme:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai2")),
column(width=3, numericInput("RowColScheme.num", label = "Num of colors:", value = 7))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'ColSideColors'",
fluidRow(
column(width=2, selectizeInput("ColClrBy",
label = "Color sample by?",
choices=NULL, multiple=FALSE,
options = list(placeholder='Hierarchical'))
),
column(width=2, numericInput("ColSideColorsSize", label = "Column Color Size:", value=2.0, step=0.1)),
conditionalPanel(
condition = "input.ColClrBy == 'Filename'",
column(width=2, selectInput("SelColSideColors",
label = "Show columns in order:",
multiple = TRUE,
choices = c(1,2,3,4,5,6),
selected = 1)),
column(width=2, sliderInput("ColSideColorsNum", label = "Modify Which Column:", min=1, max=6, value=1, ticks = F)),
# Need to fix this later
conditionalPanel(
condition = "input.ColSideColorsNum == 1",
column(width=2, selectInput("ColScheme1",
label = "Column Color 1:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai")),
column(width=2, numericInput("ColScheme1.num", label = "Num of colors:", value = 5))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 2",
column(width=2, selectInput("ColScheme2",
label = "Column Color 2:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Paired")),
column(width=2, numericInput("ColScheme2.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 3",
column(width=2, selectInput("ColScheme3",
label = "Column Color 3:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Greys")),
column(width=2, numericInput("ColScheme3.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 4",
column(width=2, selectInput("ColScheme4",
label = "Column Color 4:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "YlOrRd")),
column(width=2, numericInput("ColScheme4.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 5",
column(width=2, selectInput("ColScheme5",
label = "Column Color 5:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Pastel1")),
column(width=2, numericInput("ColScheme5.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 6",
column(width=2, selectInput("ColScheme6",
label = "Column Color 6:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai")),
column(width=2, numericInput("ColScheme6.num", label = "Num of colors:", value = 10))
)
)
)
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & (input.VisType=='PCA' || input.VisType=='t-SNE')",
fluidRow(
column(width=2, selectizeInput("pt_col",
label = "Color sample by?",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
),
conditionalPanel(
condition = "input.pt_col == 'GeneExpr'",
column(width=2, selectizeInput("pt_allgene",
label = "Select gene",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
conditionalPanel(
condition = "input.pt_col == 'NMF Feature'",
column(width=2, selectizeInput("pt_nmfgene",
label = "Select gene",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
conditionalPanel(
condition = "input.pt_col == 'Filename'",
column(width=1, selectizeInput("pt_file_grp",
label="Group#",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
),
column(width=1, selectInput("pt_sel_grp_order", label="Order", choices=c("Default", "Manual"), selected="Default")),
conditionalPanel(
condition = "input.pt_sel_grp_order == 'Manual'",
column(width=2, selectizeInput("pt_grp_order",
label="Select group order",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
)
),
conditionalPanel(
condition = "input.VisType == 'PCA'",
column(width=1, selectInput("pca_x", label="PC on X", choices=seq(1,10), selected = 1)),
column(width=1, selectInput("pca_y", label="PC on Y", choices=seq(1,10), selected = 2))
),
conditionalPanel(
condition = "input.VisType == 't-SNE'",
column(width=1, numericInput("tsne_perplexity", label = "Perplexity", value=10)),
column(width=1, numericInput("tsne_iter", label = "Iterations", value=20)),
column(width=1, numericInput("tsne_theta", label = "theta", value=0.5, step = 0.01, min = 0)),
column(width=1, numericInput("tsne_pca_num", label = "PCA #", value=50, step = 5, min = 10))
),
column(width=1, numericInput("plot_point_size", label = "DotSize", value=7)),
column(width=1, numericInput("plot_point_alpha", label = "Alpha", value=0.75, step=0.05)),
column(width=1, numericInput("plot_label_size", label = "LabelSize", value=9)),
column(width=1, checkboxInput('plot_label', 'Add label', FALSE)),
column(width=1, checkboxInput('plot_legend', 'Add legend', TRUE)),
# conditionalPanel(
# # condition = "input.pt_col == 'Filename' || input.pt_col == 'NMF Group')",
# condition = "input.pt_col == 'Filename'",
# ),
uiOutput("visualperplexity_UI"),
column(width=6, bsAlert("visualperplexityAlert"))
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & input.VisType=='Cluster Distribution'",
fluidRow(
column(width=2, numericInput("cl_alpha", label = "Alpha", value=0.4, step=0.05, max=1, min=0.05)),
column(width=2, numericInput("cl_lgsize", label = "Legend size", value=10, step=1, max=14, min=6)),
column(width=2, numericInput("cl_tickfont", label = "Tick font size", value=10, step=1, max=20, min=4)),
column(width=2, numericInput("cl_axisfont", label = "Axis font size", value=14, step=1, max=20, min=4))
)
)
),
conditionalPanel(
condition = "input.VisType == 'Heatmap'",
box(width=12, d3heatmapOutput("d3heatmap", height=700))
),
conditionalPanel(
condition = "input.VisType == 'PCA'",
box(width=6, title="2D PCA Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("pcaPlot_2D", height=500)),
box(width=6, title="3D PCA Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("pcaPlot_3D", height=500))
),
conditionalPanel(
condition = "input.VisType == 't-SNE'",
box(width=6, title="2D t-SNE Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('tsneplot_2d', height=500)),
box(width=6, title="3D t-SNE Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("tsneplot_3d", height=500))
),
conditionalPanel(
condition = "input.VisType == 'Cluster Distribution'",
box(width=4, title="Transcriptome variance", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("nmfgrp_var", height=350)),
box(width=4, title="No. of expressed genes", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('nmfgrp_expgene', height=350)),
box(width=4, title="Intra-group correlation coefficient", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('nmfgrp_coef', height=350))
)
)
),
tabItem("DESeq2",
fluidRow(
box(title="Differential Analysis Parameters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2, selectizeInput("de_group1",
label = "Reference group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group'))
),
column(width=2, selectizeInput("de_group2",
label = "Treatment group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group'))
),
column(width=2, numericInput("de_alpha", label = "alpha cutoff", min=0.01, max=0.1, value=0.05, step = 0.01)),
column(width=2, selectInput("de_conv2int", label = "Force convert to integer",
choices = c("Yes" = TRUE, "No" = FALSE),
selected = FALSE)
),
column(width=2, br(),
actionButton("runDESeq", "Run DESeq!", icon("play-circle"), class = 'act')
)
),
fluidRow(
column(width=8, DT::dataTableOutput('deseq_table')),
column(width=4, plotlyOutput('deseq_boxplot', height = "500px"))
),
fluidRow(
column(width=12, bsAlert("alert"))
)
)
)
),
tabItem("enrichment",
fluidRow(
box(title="Enrichment Analysis Parameters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2,
selectizeInput("pathway_group",
label = "Pick one NMF group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group')),
bsTooltip("pathway_group", "Choose one NMF group to perform pathway enrichment test",
"right", options = list(container = "body"))
),
column(width=2,
selectInput("species",
label = "Species",
choices = c("Human", "Mouse"),
selected = "Human"),
bsTooltip("species", "Default set to Human <br>Please select the corresponding species for your data, the program will run the conversion if necessary",
"right", options = list(container = "body"))
),
conditionalPanel(
condition = "input.EnrichType == 'GO'",
column(width=2, selectInput("goTerm",
label = "GO category",
choices = c("BP", "MF", "CC"),
selected = "MF")
)
),
column(width=2, br(),
actionButton("run_enrich", "Run Analysis", icon("play-circle"), class = 'act')
),
column(width=2, checkboxInput('enrichmoreopt', 'More Options', FALSE))
),
conditionalPanel(
condition = "input.enrichmoreopt == true",
fluidRow(
column(width=2, selectInput("rank_method",
label = "Rank genes by?",
choices = c("NMF FeatureScore"="featureScore",
"Log Fold Change" = "logFC"),
selected = "logFC")
),
column(width=2, selectInput("samedir",
label = "Need logFC in same direction",
choices = c("Yes"=TRUE, "NO"=FALSE),
selected = TRUE)
),
column(width=2, numericInput("min_rowMean",
label = "Expression cutoff:",
min=0, max=10, value=0.5, step=0.05)
),
column(width=2, numericInput("qval_cutoff",
label = "q_val cutoff:",
min=0.05, max=1, value=0.4, step=0.05)
),
column(width=2, selectInput("go_logscale",
label = "Use Log scale",
choices = c("Yes"=TRUE, "No"=FALSE),
selected = TRUE)
)
),
fluidRow(
column(width=2, selectInput("go_colorscale",
label = "Color Scheme:",
# choices = c("Greys", "YlGnBu", "Greens", "YlOrRd", "Bluered", "RdBu", "Reds", "Blues", "Picnic", "Rainbow", "Portland", "Jet", "Hot", "Blackbody", "Earth", "Electric", "Viridis"),
choices = c("YlOrRd", "YlOrBr", "YlGnBu", "PuBuGn", "Spectral", "RdYlGn", "RdYlBu", "RdGy", "RdBu", "PRGn", "BuPu", "BuGn", "Blues", "Greys", "PiYG", "BrBG"),
selected = "Spectral")
),
column(width=2, numericInput("go_xfontsize",
label = "x-axis font size:",
min=2, max=30, value=10, step=1)
),
column(width=2, numericInput("go_yfontsize",
label = "y-axis font size:",
min=2, max=30, value=8, step=1)
),
column(width=2, numericInput("go_leftmargin",
label = "left margin:",
min=50, max=500, value=200, step=10)
),
column(width=2, numericInput("go_barwidth",
label = "colorbar width:",
min=10, max=40, value=14, step=2)
),
column(width=2, br(),
downloadButton("downloadPathData", "Download Result", class="dwnld")
)
)
)
)
),
fluidRow(
column(width=4,
box(width=NULL, title="GO term", height = "500px",
DT::dataTableOutput('go_summary'),
tags$hr(),
p(
class = "text-muted",
paste("Note: Make sure you select the correct species.")
)
)
),
column(width=4,
box(width=NULL, title="Summary Plot - Greater", solidHeader=TRUE, status="info", height="500px",
plotlyOutput('goplot_hi')
)
),
column(width=4,
box(width=NULL, title="Summary Plot - Less", solidHeader=TRUE, status="info", height="500px",
plotlyOutput('goplot_lo')
)
),
conditionalPanel(
condition = "input.EnrichType == 'KEGG'",
column(width=12,
box(width=NULL, height = "850px",
imageOutput("pathviewImg")
)
)
)
)
)
)
)
dashboardPage(
header,
sidebar,
body
)
naikai/sake documentation built on Feb. 15, 2023, 11 p.m.
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library(shiny)
library(shinyBS)
library(plotly)
library(d3heatmap)
library(htmltools)
library(shinythemes)
library(shinydashboard)
library(DT)
tg <- tags$div()
tg <- attachDependencies(
tg,
htmlDependency(
name = "plotlyjs",
version = "1.13.0",
src = c(href = "https://cdn.plot.ly"),
script = "plotly-latest.min.js"
)
)
header <- dashboardHeader(
title = "SAKE"
)
sidebar <- dashboardSidebar(
sidebarMenu(id = "sidebarmenu",
menuItem("File", tabName="File", icon=icon('file-text-o')),
menuItem("Quality control", tabName="quality", icon = icon('bar-chart-o')),
menuItem("Filtering", tabName="filter", icon = icon('scissors'),
menuSubItem("Identify Variable Genes", tabName="featureSelection"),
menuSubItem("Sample Scatter Plot", tabName="scatter")
),
menuItem("NMF", tabName="NMF", icon = icon('sitemap'),
menuSubItem("NMF Run", tabName="nmfRun"),
menuSubItem("Identify Groups", tabName="nmfGroups"),
menuSubItem("Extract features", tabName="nmfFeatures")
),
menuItem("Visualization", tabName="Visualization", icon = icon('calendar', lib = "glyphicon")
),
conditionalPanel("input.sidebarmenu === 'Visualization'",
selectInput("VisType",
label = "What kind of plot?",
choices = c("Heatmap", "PCA", "t-SNE", "Cluster Distribution"),
selected = "Cluster Distribution")
),
menuItem("Differential analysis", tabName="DE", icon = icon('tasks', lib="glyphicon"),
menuSubItem("DESeq2", tabName="DESeq2")
# menuSubItem("SCDE", tabName="SCDE")
),
menuItem("Enrichment analysis", tabName="enrichment", icon = icon('cogs')
),
conditionalPanel("input.sidebarmenu === 'enrichment'",
selectInput("EnrichType",
label = "Which Category?",
choices = c("GO", "KEGG"),
selected = "KEGG")
)
)
)
body <- dashboardBody(
tags$head(tags$script(src = "google-analytics.js")),
tags$head(tags$style(".dwnld{background-color:#8E24BF;} .dwnld{color: white;} .dwnld{border-color:$9932CC;}")),
tags$head(tags$style(".act{background-color:#337AB7;} .act{color: #FFFFFF;} .act{border-color:$2E6DA4;}")),
tags$head(tags$style(".stop{background-color:#4F1E0A;} .stop{color: #FFFFFF;} .stop{border-color:#612C0E;}")),
tags$head(tg),
tabItems(
tabItem("File",
fluidRow(
tabBox( width=5, id = "tabset1", height = "575px",
tabPanel("Upload file",
selectInput("selectfile",
label = "How do you want to upload the data?",
choices = list("Select from preloaded data"='preload',
"Upload rawdata"='upload',
"Upload saved NMF run"='saved'),
selected = "saved"),
conditionalPanel(
condition = "input.selectfile == 'upload'",
fileInput('file1', 'Choose text File',
accept=c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'text/plain',
'.csv',
'.tsv',
'.out'))
),
conditionalPanel(
condition = "input.selectfile == 'preload'",
selectizeInput(inputId = 'inputdata',
label = 'Choose DataSet',
choices = filenames,
options = list(
placeholder = 'Please select a data set',
onInitialize = I('function() { this.setValue(""); }')
)
)
),
conditionalPanel(
condition = "input.selectfile == 'upload' || input.selectfile == 'preload'",
tags$hr(),
checkboxInput('header', 'Header', TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
selected='\t')
),
conditionalPanel(
condition = "input.selectfile == 'saved'",
fileInput('rda', 'Choose saved RData',
accept=c('.rda',
'.RData'))
)
),
tabPanel("System setup",
selectInput("ncores", label = "Specify number of cores to run analysis",
choices = c(1,2,4,8,16,24,32,48,64),
selected = 16)
)
),
column(width = 7,
box(width = NULL, title="Preview Raw Data", status = "warning",
DT::dataTableOutput('rawdatatbl', height = "439px"),
tags$hr(),
p(
class = "text-muted",
paste("Note: You can preview the loaded data, ",
"make sure the format is what you want to then proceed."
)
)
)
)
),
fluidRow(
infoBoxOutput("nameBox"),
infoBoxOutput("sampleBox"),
infoBoxOutput("featureBox")
)
),
tabItem("quality",
fluidRow(
box(title="Datatable", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=3,
selectInput("normdata", label = "Normalization",
choices = list("RPM normalization" = "takeRPM",
"DESeq size factor normalization" = "takesizeNorm",
"Upper Quartile normalization" = "takeuq",
"None" = "none"),
selected = "none")
),
column(width=3,
selectInput("transformdata", label = "Transformation",
choices = list("Variance Stablizing Transformation" = "takevst",
"Log transformation" = "takelog",
"None" = "none"),
selected = "none")
),
column(width=2, br(),
actionButton("run_transf", "Run Transform", icon("play-circle"), class = 'act')
),
column(width=2, br(),
downloadButton("dl_transformdata", "Download Data", class="dwnld")
)
)
)
),
fluidRow(
column(width=6,
box(title="Read Distribution", width=NULL, solidHeader=TRUE, status="info", height="570px",
plotlyOutput("readDistrib", height=500)
)
),
column(width=6,
box(title="Samples", width=NULL, solidHeader=TRUE, status="info", height="350px",
DT::dataTableOutput("selsamp_tb")
),
box(title="Filter", width=NULL, solidHeader=TRUE, status="info", height="200px",
verbatimTextOutput("selsamp_txt"),
actionButton("run_filtsamp", "Submit", icon("play-circle"), class = 'act')
)
)
)
),
tabItem("featureSelection",
fluidRow(
box(width=12, collapsible = TRUE,
fluidRow(
column(width=2,
selectInput("list_type",
label = "How to extract genes",
choices = c("Whole transcriptome",
"Top Ranks",
"Upload gene list"),
selected = "Top Ranks"
)
),
conditionalPanel(
condition = "input.list_type == 'Top Ranks'",
column(width=2, selectInput("orders",
label = "Rank order by",
choices = c("top", "bottom"),
selected = "top")),
column(width=2, selectInput("math",
label = "Arithmetic",
choices = c("mean", "median", "mad", "var"),
selected = "mad")),
column(width=4, sliderInput("top.num",
label = "How many genes to select", ticks = FALSE,
min=500, max=20000, value=5000, step = 500)
)
),
conditionalPanel(
condition = "input.list_type == 'Upload gene list'",
column(width=3,
fileInput('genefile', 'Choose gene list file',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv'))
)
)
)
)
),
fluidRow(
column(width=6,
featureUI("sample", title = "Sample Correlation")
),
column(width=6,
networkUI("gene", title = "Gene Network")
)
)
),
tabItem("scatter",
fluidRow(
box(title="Parameters", width=12, solidHeader = TRUE, status="success",
fluidRow(
column(width=2, selectizeInput("cor_sample1", label = "Sample on X-axis", choices=NULL, multiple=FALSE)),
column(width=2, selectizeInput("cor_sample2", label = "Sample on Y-axis", choices=NULL, multiple=FALSE)),
column(width=2, checkboxInput("scatter_log", label = "Log transformation", TRUE)),
column(width=2, checkboxInput("show_r2", label = "Show R2", FALSE)),
column(width=2, checkboxInput("show_fc", label = "Show Fold-Change line", TRUE))
)
),
box(width=6, height="520px", plotlyOutput('scatterPlot')),
box(width=6, height="520px", DT::dataTableOutput('scatterdatatbl'))
)
),
tabItem("nmfRun",
fluidRow(
box(title="Estimate Number of Clusters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2,
selectInput("mode",
label = "Which module?",
choices = c("Estimate number of k" = "estim",
"Real run" = "real"),
selected = "real"),
bsTooltip("mode", "We recommend you run estimate number of cluster (k) first <br> Then use the best estimate cluster (k) for real run",
"right", options = list(container = "body"))
),
column(width=2,
numericInput("num_cluster",
label = "Estimate clusters",
min=2, max=10, value=3, step=1),
bsTooltip("num_cluster", "For Estimate number of k, it will run NMF from 2 to k clusters <br><br> For Real run, it will run NMF for that specific k cluster",
"right", options = list(container = "body"))
),
column(width=2,
numericInput("nrun",
label = "Num of runs",
min=10, max=200, value=7, step=10),
bsTooltip("nrun", "Recommend 20-30 for estimate k <br> 50-100 for real run",
"right", options = list(container = "body"))
),
column(width=2,
actionButton("runNMF", "Run NMF", icon("play-circle"), class = 'act'),
checkboxInput('nmf_runmoreopt', 'More Options', FALSE)
),
uiOutput("dlNMF_UI"),
column(width=6, bsAlert("NMFAlert")),
column(width=6, bsAlert("YabiAlert")),
bsModal("nmfModal1", "Warning!\nYour gene size is above 1000, it will take longer than usual.\nAre you sure you want to continue?",
trigger = "", size = "small",
textInput("mailto", label = "To:", placeholder = "To:"),
actionButton("nmf_yabi_yes", 'Yes'),
actionButton("nmf_yabi_no", 'No')
)
),
fluidRow(
conditionalPanel(
condition = "input.nmf_runmoreopt == true",
column(width=2,
selectInput("algorithm",
label = "NMF algorithm",
choices = c("brunet", "lee", "nsNMF", "KL", "Frobenius"),
selected = "brunet"),
bsTooltip("algorithm", "Different NMF algorithms",
"right", options = list(container = "body"))
),
column(width=2,
selectInput("nmf_seed",
label = "Random seed",
choices = c("Yes"=0, "No"=123211),
selected = 123211),
bsTooltip("nmf_seed", "Whether to use random starting condition for NMF run <br> Default: fixed seed",
"right", options = list(container = "body"))
)
)
)
)
),
conditionalPanel(
condition = "input.mode == 'estim'",
fluidRow(
box(title="NMF Plot", width=8, solidHeader = TRUE, status="info", height="1000px",
collapsible = TRUE,
fluidRow(
column(width=4, selectInput("estimtype",
label = "What kind of plot?",
choices = c("Quality measures" = "quality",
"Consensus matrics" = "consensus"),
selected = "consensus")
),
column(width=3, br(),
downloadButton("dl_nmf_estimplot", "Save plot", class="dwnld")
),
fluidRow(
column(width=4,
br(),
checkboxInput('dl_nmf_estimmoreopt', 'More Options', FALSE),
conditionalPanel(
condition = "input.dl_nmf_estimmoreopt == true",
strong("PDF size (inches):"),
sliderInput(inputId="estim_pdf_w", label = "width:", min=3, max=20, value=14, ticks=F),
sliderInput(inputId="estim_pdf_h", label = "height:", min=3, max=20, value=14, ticks=F)
)
)
)
),
fluidRow(
column(width=12,
plotOutput('estimPlot')
)
)
),
box(title="Stats", width=4, solidHeader = TRUE, status="warning", height="1000px",
DT::dataTableOutput('estimSummary')
)
)
),
conditionalPanel(
condition = "input.mode == 'real'",
fluidRow(
box(title="NMF Plot", width=8, solidHeader = TRUE, status="info", height = "1000px",
fluidRow(
column(width=4, selectInput("plottype",
label = "What kind of plot?",
choices = c("samples", "features", "consensus"),
selected = "samples")
),
column(width=3, br(),
downloadButton("dl_nmf_realplot", "Save plot", class="dwnld")
),
fluidRow(
column(width=4,
br(),
checkboxInput('dl_nmf_realmoreopt', 'More Options', FALSE),
conditionalPanel(
condition = "input.dl_nmf_realmoreopt == true",
checkboxInput('nmfplot_silhouette', 'Match silhouette order', TRUE),
strong("PDF size (inches):"),
sliderInput(inputId="real_pdf_w", label = "width:", min=3, max=20, value=14, ticks=F),
sliderInput(inputId="real_pdf_h", label = "height:", min=3, max=20, value=14, ticks=F)
)
)
)
),
fluidRow(
column(width=12, plotOutput('nmfplot'))
)
),
box(title="Silhouette Plot", width=4, solidHeader=TRUE, status="info", height="1000px",
plotOutput('silhouetteplot')
)
)
)
),
tabItem("nmfGroups",
fluidRow(
box(title="Identify Groups", width=4, solidHeader=TRUE, status="success", height = "740px",
fluidRow(
column(width=12,
selectInput("predict",
label = "How to assign NMF groups?",
choices = c("consensus", "samples"),
selected = "samples"),
checkboxInput('nmf_prob', 'Change dot size based on probability?', FALSE),
bsTooltip("nmf_prob", "Samples with higher uncertainty (lower probability of being correctly assigned) will become larger dots",
"right", options = list(container = "body")),
DT::dataTableOutput('nmfGroups')
)
)
),
tabBox(title=tagList(shiny::icon("th-large"), "t-SNE Plot"),
width=8, id = "tabset2", side = "right", height = "740px",
tabPanel("2D",
column(width=2, numericInput("nmftsne_perplexity", label = "Perplexity", value=10)),
column(width=2, br(),
actionButton("run_nmftSNE", "Run t-SNE!", icon("play-circle"), class = "act")
),
uiOutput("dlperplexity_UI"),
column(width=6, bsAlert("perplexityAlert")),
column(width=12,
plotlyOutput('nmftsneplot', height=600, width=600)
)
)
)
)
),
tabItem("nmfFeatures",
fluidRow(
box(title="Extract Features", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=3,
selectInput("select_method",
label = "Feature selection method",
choices = c("Total"="total", "By default"="default", "rank"="rank"),
selected = "default")
),
conditionalPanel(
condition = "input.select_method == 'default' | input.select_method == 'rank'",
column(width=3,
selectInput("select_feature_num",
label = "Number of features from each group",
choices = c("By default"=0, "10"=10, "20"=20, "30"=30, "50"=50, "100"=100, "200"=200, "500"=500, "1000"=1000, "1500"=1500, "2000"=2000, "3000"=3000, "5000"=5000),
selected = 0)
),
conditionalPanel(
condition = "input.select_method == 'rank'",
column(width=3,
selectInput("select_math",
label = "Rank based on:",
choices = c("mean", "median", "mad", "var"),
selected = "mad")
),
column(width=3,
numericInput("select_FScutoff",
label = "FeatureScore cutoff",
min=0.5, max=1, value=0.86, step = 0.01)
)
)
),
column(width=1, selectInput("sel_vioplot",
label = "Plot type",
choices = c("box", "violin"),
selected = "violin")
),
column(width=1, selectInput("sel_vioscale",
label = "Y-scale",
choices = c("Raw", "log2", "log10"),
selected = "log2")
),
checkboxInput('nmf_feature_moreopt', 'More Options', FALSE)
),
conditionalPanel(
condition = "input.nmf_feature_moreopt == true",
fluidRow(
column(width=2, selectInput("sel_grp",
label = "Order:",
choices = c("Default", "Manual"),
selected = "Default")
),
conditionalPanel(
condition = "input.sel_grp == 'Manual'",
column(width=2, selectizeInput("sel_grp_order",
label="Select group order",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
column(width=1, numericInput("sel_xfont_size",
label = "Xlab size",
min=1, max=20, value=11, step=1)
),
column(width=1, numericInput("sel_yfont_size",
label = "Ylab size",
min=1, max=20, value=12, step=1)
),
column(width=1, numericInput("sel_ncol",
label = "# Column",
min=1, max=10, value=1, step=1)
),
column(width=1, numericInput("sel_ymax",
label = "Ymax",
min=0, max=1000000, value=0, step=1)
),
column(width=1, numericInput("sel_ymin",
label = "Ymin",
min=0, max=1000000, value=0, step=1)
),
column(width=1, numericInput("sel_title_size",
label = "Title size",
min=1, max=20, value=14, step=1)
),
column(width=2, numericInput("sel_legend_size",
label = "Legend size",
min=1, max=20, value=9, step=1)
),
column(width=1, checkboxInput("sel_show_legend", label="Show Legend", TRUE))
)
),
fluidRow(
column(width=7, DT::dataTableOutput('nmfFeatures')),
column(width=5, plotlyOutput('nmf_vioplot', height = "500px"))
# column(width=5, plotlyOutput('nmf_vioplot'))
)
)
)
),
tabItem("Visualization",
fluidRow(
box(width=12,
fluidRow(
column(width=3, selectInput("heatmapGene",
label = "How to select genes?",
choices = c("From NMF features" = "nmf",
"Ranks from data" = "rank",
"Upload gene list" = "upload",
"Manually select genes" = "manual",
"Preloaded gene list" = "preload"),
selected = "rank")
),
conditionalPanel(
condition = "input.heatmapGene == 'upload'",
column(width=3, fileInput('heatmapGeneFile', 'Choose text File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv',
'.out'))
)
),
conditionalPanel(
condition = "input.heatmapGene == 'rank'",
column(width=3, selectInput("heat.math",
label = "Rank by which metrics?",
choices = c("Mean Expression" = "mean",
"Median Expression" = "median",
"Median Absolute Deviation" = "mad",
"Variance" = "var"),
selected = "mad")
),
column(width=2, selectInput("heat.top.num",
label = "How many top genes:",
choices = c(seq(10,100,10), seq(200, 1000, 100), seq(2000, 15000, 1000)),
selected = 5000)
)
),
conditionalPanel(
condition = "input.heatmapGene == 'preload'",
column(width=3,
selectizeInput("predefined_list",
label = "Select from predefined lists",
choices = list(
Melanoma = c(`Keith Hoek (105 genes, 2006, Pigment Cell & Melanoma)`='Hoek',
`Widmer (Hoek's group) (97 genes, 2012, Pigment Cell & Melanoma)`='Widmer',
`Stein Aert (core 59 genes, 2015, Nature Communication)`='Stein_core',
`Stein Aert (whole 1412 genes, 2015, Nature Communication)`='Stein',
`SKCM NMF-K3 (53 genes, 2015, TCGA)`='SKCM-NMF-K3',
`SKCM NMF-K3-VST (101 genes, 2015, TCGA)`='SKCM-NMF-K3-VST-1000nrun',
`SKCM NMF-K4 (61 genes, 2015, TCGA)`='SKCM-NMF-K4',
`Stephanie Kreis (20 miRNAs, 2015, Oncotarget)`='Melanoma-20miRNA'
),
Hallmark = c(`Cell Cycle phases`='CellCycle',
`Regulation of Cell cycle`='RegCC',
`EMT`='EMT',
`KRAS`='KRAS',
`WNT`='WNT'
),
Others = c(`Resistance markers (combined from literature search)`='PubResistList',
`Resistance markers (Only select core)`='PubResistList_core',
`My 4 groups Gene List (May modify later)`='MyFourGroups',
`Aviv Regev cell cycle genes`='Aviv'
),
Mouse = c(`Cell Cycle phases`='Mouse_CellCycle',
`Regulation of Cell cycle`='Mouse_RegCellCycle'
),
Breast = c(`PAM50`='PAM50',
`BRCA-NMF-K5`="BRCA-NMF-K5"
)
),
selected = 'EMT', multiple=TRUE
)
)
),
conditionalPanel(
condition = "input.heatmapGene == 'manual'",
column(width=3, selectizeInput("manual_genes",
label = "Select your favorite genes",
choices=NULL, multiple=TRUE,
options = list(placeholder='Type in gene name, e.g. MYC, ERBB2')
)
)
),
conditionalPanel(
condition = "input.VisType == 'Heatmap' ",
column(width = 3, br(),
downloadButton("dl_heatmap", "Download", class = "butt"),
tags$head(tags$style(".butt{background-color:#8E24BF;} .butt{color: white;} .butt{border-color:$9932CC;}"))
)
),
column(width=2, checkboxInput('heatmoreopt', 'More Options', FALSE)),
conditionalPanel(
condition = "input.VisType == 't-SNE' ",
column(width=2, actionButton("runtSNE", "Run t-SNE!", icon("play-circle"), width="100px", class = 'act'))
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & input.VisType == 'Heatmap' ",
fluidRow(
column(width=6,
selectInput("heat_opttype",
label = "Which paramter type",
choices = list('Figure adjustment' = c(`Column Side Colors` = 'ColSideColors',
`Row Side Colors` = 'RowSideColors',
`Heatmap Color Scheme` = 'heatcolor',
`PDF Size` = 'pdf',
`Label Size and Apperance` = 'labels'),
'Clustering' = c(`Cluster Row or Column` = 'cluster',
`How to Scale Data` = 'scale',
`Distance and Linkage` = 'distnlink')),
selected = "cluster")
)
),
conditionalPanel(
condition = "input.heat_opttype == 'cluster'",
fluidRow(
column(width=3,
selectizeInput("OrdCol",
label = "Cluster column?",
choices=NULL, multiple=FALSE,
options = list(placeholder='Hierarchical')),
conditionalPanel(
condition = "input.OrdCol == 'Filename'",
numericInput("sortcolumn_num", label = "Sort by which group:", value = 1, min = 1)
),
conditionalPanel(
condition = "input.OrdCol == 'GeneExpr'",
selectizeInput("sortgene_by",
label = "Select a gene to sort on",
choices=NULL, multiple=FALSE,
options = list(placeholder='Type in gene name, e.g. MYC, ERBB2'))
)
),
column(width=3, selectInput("OrdRow", "Cluster row?",
choices = list("Default ordering" = "default",
"Hierarchical" = "hier"),
selected = "hier"))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'scale'",
fluidRow(
column(width=3, selectInput("scale_method", label = "How to scale data:", choices = c("mean", "median"), selected = "mean")),
column(width=4, checkboxInput("scale_first", "Scale data before dendrogram?", FALSE))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'distnlink'",
fluidRow(
column(width=3, selectInput("linkage",
label = "Which linkage methods?",
choices = c("average", "centroid", "complete","mcquitty", "median", "ward.D", "ward.D2", "single"),
selected = "ward.D2")),
column(width=3, selectInput("distance",
label = "Which distance methods?",
choices = c("binary", "canberra","euclidean", "manhattan", "maximum", "minkowski"),
selected = "euclidean"))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'labels'",
fluidRow(
column(width=2, textInput("title", label= "Figure title", value=NULL)),
column(width=2, numericInput("cexRow", label = "Row LabelSize:", value=0.5, step=0.1)),
column(width=2, numericInput("cexCol", label = "Col LabelSize:", value=0.4, step=0.1)),
column(width=3, selectInput('row_legend', label='Show legend for RowSide Colors?',
choices = c("Yes" = TRUE, "No" = FALSE))),
column(width=3, selectInput('col_legend', label='Show legend for ColumnSide Colors?',
choices = c("Yes" = TRUE, "No" = FALSE)))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'heatcolor'",
fluidRow(
column(width=3, selectInput("heatColorScheme",
label = "Heat map color scheme",
choices = c("RdBu_Brewer", "RdYlBu_Brewer", "Red_Green", "Red_Blue","Rainbow", "Black_White"),
selected = "RdBu_Brewer")),
column(width=3, numericInput("color_num", label = "Num", value = 9, min=5))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'pdf'",
fluidRow(
column(width=3, sliderInput("heat_pdf_wd", label = "Width (inch)", value=14, min=5, max=20, ticks=F)),
column(width=3, sliderInput("heat_pdf_ht", label = "Height (inch)", value=12, min=5, max=20, ticks=F))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'RowSideColors'",
fluidRow(
column(width=3, selectInput("RowColScheme",
label = "Row Color Scheme:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai2")),
column(width=3, numericInput("RowColScheme.num", label = "Num of colors:", value = 7))
)
),
conditionalPanel(
condition = "input.heat_opttype == 'ColSideColors'",
fluidRow(
column(width=2, selectizeInput("ColClrBy",
label = "Color sample by?",
choices=NULL, multiple=FALSE,
options = list(placeholder='Hierarchical'))
),
column(width=2, numericInput("ColSideColorsSize", label = "Column Color Size:", value=2.0, step=0.1)),
conditionalPanel(
condition = "input.ColClrBy == 'Filename'",
column(width=2, selectInput("SelColSideColors",
label = "Show columns in order:",
multiple = TRUE,
choices = c(1,2,3,4,5,6),
selected = 1)),
column(width=2, sliderInput("ColSideColorsNum", label = "Modify Which Column:", min=1, max=6, value=1, ticks = F)),
# Need to fix this later
conditionalPanel(
condition = "input.ColSideColorsNum == 1",
column(width=2, selectInput("ColScheme1",
label = "Column Color 1:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai")),
column(width=2, numericInput("ColScheme1.num", label = "Num of colors:", value = 5))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 2",
column(width=2, selectInput("ColScheme2",
label = "Column Color 2:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Paired")),
column(width=2, numericInput("ColScheme2.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 3",
column(width=2, selectInput("ColScheme3",
label = "Column Color 3:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Greys")),
column(width=2, numericInput("ColScheme3.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 4",
column(width=2, selectInput("ColScheme4",
label = "Column Color 4:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "YlOrRd")),
column(width=2, numericInput("ColScheme4.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 5",
column(width=2, selectInput("ColScheme5",
label = "Column Color 5:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "Pastel1")),
column(width=2, numericInput("ColScheme5.num", label = "Num of colors:", value = 10))
),
conditionalPanel(
condition = "input.ColSideColorsNum == 6",
column(width=2, selectInput("ColScheme6",
label = "Column Color 6:",
choices = c("naikai", "naikai2", "Set1", "Set2", "Set3","YlGn", "YlGnBu", "YlOrRd", "Greys", "Pastel1", "Pastel2", "Paired", "Dark2"),
selected = "naikai")),
column(width=2, numericInput("ColScheme6.num", label = "Num of colors:", value = 10))
)
)
)
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & (input.VisType=='PCA' || input.VisType=='t-SNE')",
fluidRow(
column(width=2, selectizeInput("pt_col",
label = "Color sample by?",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
),
conditionalPanel(
condition = "input.pt_col == 'GeneExpr'",
column(width=2, selectizeInput("pt_allgene",
label = "Select gene",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
conditionalPanel(
condition = "input.pt_col == 'NMF Feature'",
column(width=2, selectizeInput("pt_nmfgene",
label = "Select gene",
choices=NULL, multiple=FALSE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
),
conditionalPanel(
condition = "input.pt_col == 'Filename'",
column(width=1, selectizeInput("pt_file_grp",
label="Group#",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
),
column(width=1, selectInput("pt_sel_grp_order", label="Order", choices=c("Default", "Manual"), selected="Default")),
conditionalPanel(
condition = "input.pt_sel_grp_order == 'Manual'",
column(width=2, selectizeInput("pt_grp_order",
label="Select group order",
choices=NULL, multiple=TRUE,
options = list(
onInitialize = I('function() { this.setValue(""); }')
))
)
)
),
conditionalPanel(
condition = "input.VisType == 'PCA'",
column(width=1, selectInput("pca_x", label="PC on X", choices=seq(1,10), selected = 1)),
column(width=1, selectInput("pca_y", label="PC on Y", choices=seq(1,10), selected = 2))
),
conditionalPanel(
condition = "input.VisType == 't-SNE'",
column(width=1, numericInput("tsne_perplexity", label = "Perplexity", value=10)),
column(width=1, numericInput("tsne_iter", label = "Iterations", value=20)),
column(width=1, numericInput("tsne_theta", label = "theta", value=0.5, step = 0.01, min = 0)),
column(width=1, numericInput("tsne_pca_num", label = "PCA #", value=50, step = 5, min = 10))
),
column(width=1, numericInput("plot_point_size", label = "DotSize", value=7)),
column(width=1, numericInput("plot_point_alpha", label = "Alpha", value=0.75, step=0.05)),
column(width=1, numericInput("plot_label_size", label = "LabelSize", value=9)),
column(width=1, checkboxInput('plot_label', 'Add label', FALSE)),
column(width=1, checkboxInput('plot_legend', 'Add legend', TRUE)),
# conditionalPanel(
# # condition = "input.pt_col == 'Filename' || input.pt_col == 'NMF Group')",
# condition = "input.pt_col == 'Filename'",
# ),
uiOutput("visualperplexity_UI"),
column(width=6, bsAlert("visualperplexityAlert"))
)
),
conditionalPanel(
condition = "input.heatmoreopt == true & input.VisType=='Cluster Distribution'",
fluidRow(
column(width=2, numericInput("cl_alpha", label = "Alpha", value=0.4, step=0.05, max=1, min=0.05)),
column(width=2, numericInput("cl_lgsize", label = "Legend size", value=10, step=1, max=14, min=6)),
column(width=2, numericInput("cl_tickfont", label = "Tick font size", value=10, step=1, max=20, min=4)),
column(width=2, numericInput("cl_axisfont", label = "Axis font size", value=14, step=1, max=20, min=4))
)
)
),
conditionalPanel(
condition = "input.VisType == 'Heatmap'",
box(width=12, d3heatmapOutput("d3heatmap", height=700))
),
conditionalPanel(
condition = "input.VisType == 'PCA'",
box(width=6, title="2D PCA Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("pcaPlot_2D", height=500)),
box(width=6, title="3D PCA Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("pcaPlot_3D", height=500))
),
conditionalPanel(
condition = "input.VisType == 't-SNE'",
box(width=6, title="2D t-SNE Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('tsneplot_2d', height=500)),
box(width=6, title="3D t-SNE Plot", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("tsneplot_3d", height=500))
),
conditionalPanel(
condition = "input.VisType == 'Cluster Distribution'",
box(width=4, title="Transcriptome variance", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput("nmfgrp_var", height=350)),
box(width=4, title="No. of expressed genes", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('nmfgrp_expgene', height=350)),
box(width=4, title="Intra-group correlation coefficient", solidHeader=TRUE, status="info", collapsible = TRUE, plotlyOutput('nmfgrp_coef', height=350))
)
)
),
tabItem("DESeq2",
fluidRow(
box(title="Differential Analysis Parameters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2, selectizeInput("de_group1",
label = "Reference group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group'))
),
column(width=2, selectizeInput("de_group2",
label = "Treatment group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group'))
),
column(width=2, numericInput("de_alpha", label = "alpha cutoff", min=0.01, max=0.1, value=0.05, step = 0.01)),
column(width=2, selectInput("de_conv2int", label = "Force convert to integer",
choices = c("Yes" = TRUE, "No" = FALSE),
selected = FALSE)
),
column(width=2, br(),
actionButton("runDESeq", "Run DESeq!", icon("play-circle"), class = 'act')
)
),
fluidRow(
column(width=8, DT::dataTableOutput('deseq_table')),
column(width=4, plotlyOutput('deseq_boxplot', height = "500px"))
),
fluidRow(
column(width=12, bsAlert("alert"))
)
)
)
),
tabItem("enrichment",
fluidRow(
box(title="Enrichment Analysis Parameters", width=12, solidHeader=TRUE, status="success",
fluidRow(
column(width=2,
selectizeInput("pathway_group",
label = "Pick one NMF group",
choices=NULL, multiple=FALSE,
options = list(placeholder='Choose a NMF group')),
bsTooltip("pathway_group", "Choose one NMF group to perform pathway enrichment test",
"right", options = list(container = "body"))
),
column(width=2,
selectInput("species",
label = "Species",
choices = c("Human", "Mouse"),
selected = "Human"),
bsTooltip("species", "Default set to Human <br>Please select the corresponding species for your data, the program will run the conversion if necessary",
"right", options = list(container = "body"))
),
conditionalPanel(
condition = "input.EnrichType == 'GO'",
column(width=2, selectInput("goTerm",
label = "GO category",
choices = c("BP", "MF", "CC"),
selected = "MF")
)
),
column(width=2, br(),
actionButton("run_enrich", "Run Analysis", icon("play-circle"), class = 'act')
),
column(width=2, checkboxInput('enrichmoreopt', 'More Options', FALSE))
),
conditionalPanel(
condition = "input.enrichmoreopt == true",
fluidRow(
column(width=2, selectInput("rank_method",
label = "Rank genes by?",
choices = c("NMF FeatureScore"="featureScore",
"Log Fold Change" = "logFC"),
selected = "logFC")
),
column(width=2, selectInput("samedir",
label = "Need logFC in same direction",
choices = c("Yes"=TRUE, "NO"=FALSE),
selected = TRUE)
),
column(width=2, numericInput("min_rowMean",
label = "Expression cutoff:",
min=0, max=10, value=0.5, step=0.05)
),
column(width=2, numericInput("qval_cutoff",
label = "q_val cutoff:",
min=0.05, max=1, value=0.4, step=0.05)
),
column(width=2, selectInput("go_logscale",
label = "Use Log scale",
choices = c("Yes"=TRUE, "No"=FALSE),
selected = TRUE)
)
),
fluidRow(
column(width=2, selectInput("go_colorscale",
label = "Color Scheme:",
# choices = c("Greys", "YlGnBu", "Greens", "YlOrRd", "Bluered", "RdBu", "Reds", "Blues", "Picnic", "Rainbow", "Portland", "Jet", "Hot", "Blackbody", "Earth", "Electric", "Viridis"),
choices = c("YlOrRd", "YlOrBr", "YlGnBu", "PuBuGn", "Spectral", "RdYlGn", "RdYlBu", "RdGy", "RdBu", "PRGn", "BuPu", "BuGn", "Blues", "Greys", "PiYG", "BrBG"),
selected = "Spectral")
),
column(width=2, numericInput("go_xfontsize",
label = "x-axis font size:",
min=2, max=30, value=10, step=1)
),
column(width=2, numericInput("go_yfontsize",
label = "y-axis font size:",
min=2, max=30, value=8, step=1)
),
column(width=2, numericInput("go_leftmargin",
label = "left margin:",
min=50, max=500, value=200, step=10)
),
column(width=2, numericInput("go_barwidth",
label = "colorbar width:",
min=10, max=40, value=14, step=2)
),
column(width=2, br(),
downloadButton("downloadPathData", "Download Result", class="dwnld")
)
)
)
)
),
fluidRow(
column(width=4,
box(width=NULL, title="GO term", height = "500px",
DT::dataTableOutput('go_summary'),
tags$hr(),
p(
class = "text-muted",
paste("Note: Make sure you select the correct species.")
)
)
),
column(width=4,
box(width=NULL, title="Summary Plot - Greater", solidHeader=TRUE, status="info", height="500px",
plotlyOutput('goplot_hi')
)
),
column(width=4,
box(width=NULL, title="Summary Plot - Less", solidHeader=TRUE, status="info", height="500px",
plotlyOutput('goplot_lo')
)
),
conditionalPanel(
condition = "input.EnrichType == 'KEGG'",
column(width=12,
box(width=NULL, height = "850px",
imageOutput("pathviewImg")
)
)
)
)
)
)
)
dashboardPage(
header,
sidebar,
body
)
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