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app.R
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# ShinySDAscSeq (ShinSASe): A Shiny template for scRNASeq analysis processed with SDA
library(shiny)
library(rclipboard)
library(shinydashboard)
library(shiny)
library(ggplot2)
library(data.table)
library(ggrepel)
library(viridis)
library(ggnewscale)
library(RColorBrewer)
library(grid)
library(gridExtra)
library(dplyr)
library(ggpubr)
library(Seurat)
source("./Fxs.R")
# list2env(readRDS( "./data/ShinyServerDataLS.rds"), envir = globalenv())
#ShinyServerDataLS is a list of data:
#"MetaDF" a dataframe with cell barcode + meta data including 1 dimreduc (x,y) like tsne. The other paramters differ per study so custom.
#"results" : an SDA object post processing and label correction holds the cell scores and gene loading
#"chromosome.lengths" : a dataframe with "chromosome", "length", "length_padded", "genomic_offset", "center" from mapping genes to biomart
#"StatFac": A dataframe of "SDA" comp and annotations found in processing, labels to be shown when a comp is selected
#"GO_data": a list of GO annotations, 1 per every component in the resuts
#"gene_locations": from biomart
#"col_vector" : vector of colors to be used
ui <- dashboardPage(skin="red",
dashboardHeader(title = "ShinSaSe"
# ,
# dropdownMenu(type = "messages",
# messageItem(
# from = "A",
# message = "Task 1 due."
# ),
# messageItem(
# from = "B",
# message = "question?",
# icon = icon("question"),
# time = "13:45"
# ),
# messageItem(
# from = "C",
# message = "Woohoo!",
# icon = icon("life-ring"),
# time = "2014-12-01"
# )
# ),
# dropdownMenu(type = "notifications",
# notificationItem(
# text = "ABC",
# icon("users")
# ),
# notificationItem(
# text = "XYZ",
# icon("rep"),
# status = "success"
# ),
# notificationItem(
# text = "IJK",
# icon = icon("exclamation-triangle"),
# status = "warning"
# )
# ),
# #red, yellow, aqua, blue, light-blue, green, navy, teal, olive, lime, orange, fuchsia, purple, maroon, black.
# dropdownMenu(type = "tasks", badgeStatus = "success",
# taskItem(value = 99, color = "green",
# "Preprocessing"
# ),
# taskItem(value = 99, color = "aqua",
# "SDA processing"
# ),
# taskItem(value = 99, color = "yellow",
# "DE analysis"
# ),
# taskItem(value = 70, color = "olive",
# "Documentation"
# ),
# taskItem(value = 15, color = "black",
# "Manuscript"
# ),
# taskItem(value = 70, color = "red",
# "Overall project"
# )
# )
),
dashboardSidebar(
sidebarMenu(
menuItem("Main Tab", tabName = "combodash", icon = icon("dashboard"),
badgeLabel = "underconst.", badgeColor = "yellow"),
menuItem("Gene expr stat. sig", tabName = "geneexprstatsig", icon = icon("affiliatetheme"),
badgeLabel = "soon", badgeColor = "red"),
menuItem("Tab 3. (dev)", tabName = "somadash", icon = icon("allergies"),
badgeLabel = "soon", badgeColor = "red"),
menuItem("Tab 3. (dev)", tabName = "pseudotime", icon = icon("arrows-alt"),
badgeLabel = "soon", badgeColor = "red"),
menuItem("Enrichment Analysis", tabName = "Enrichment", icon = icon("dashboard"),
badgeLabel = "underconst.", badgeColor = "yellow"),
menuItem("Source code", icon = icon("file-code-o"),
href = "https://")
)
),
dashboardBody(
tags$head(
tags$style(HTML("
.content-wrapper {
background-color: black !important;
}
.main-sidebar {
background-color: black !important;
}
"))),
##### tab items------------------
tabItems(
##### tab1------------------
# First tab content
tabItem(tabName = "combodash",
fluidRow(
box(title = "load_SeuratSDA_Obj", status = "warning", solidHeader = T,
textInput("SDAroot", "Path to SDA folders. Expects dimnames in one dir up.",
value ="../../../Expts/TetCombo3/data"), #/Tet_SDADGE_DropSim_mi10000_nc40_N21509_rep1
uiOutput("select.folder"),
actionButton("loadSDA", "Load SDA"),
width = 12),
valueBoxOutput("cellinfo1", width = 6),
valueBoxOutput("GeneName", width = 6),
box(
title = "Inputs", status = "warning", solidHeader = TRUE,
#"Box content here", br(), "More box content",
#sliderInput("ComponentN", "Slider input:", 1, 40, 1),
textInput("ComponentNtext", "Numerical input:", "1"),
textInput("Genetext", "Text input:", "CD69"),
radioButtons("compsIn", "Component Set", # ADD or REMOVE depending on what is in the data
c("True-Signal" = "TrueSigComps",
"Noise" = "NoiseComps"
)),
width = 2),
box(title = "PlotMeta", status = "warning", solidHeader = TRUE,
radioButtons("data", "Data origin:", # ADD or REMOVE depending on what is in the data
c("tSNE - raw SDA-CellScores" = "tsneCSraw",
"tSNE - batch-removed SDA-CellScores" = "tsneCSbr"
)),
radioButtons("metaselect", "Metadata Selection:",
c("EXP.ID" = "EXP.ID",
"SubjID" = "SubjectId",
"SampleDate" = "SampleDate",
#"Cluster" = "CustClus",
#"Cluster2" = "CustClus2",
"BarcodePrefix" = "BarcodePrefix",
"SingleR_Labels" = "SingleR_Labels",
"Unsup_Clust_BRtSNE" = "Unsup_Clust_BRtSNE"
)),
width = 5),
box(title = "CompControl", status = "warning", solidHeader = TRUE,
textInput("NoOfGenes", "No. of Genes to output:", "20"),
actionButton("C2Cpos", "Copy2ClipPosGenes"),
actionButton("C2Cneg", "Copy2ClipNegGenes"),
actionButton("PrevSDA", "Prev SDA"),
actionButton("NextSDA", "Next SDA"),
width = 5
)
),
box(
#actionButton("button", "Next"),
title = "Cell score", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot1"), #plotlyOutput
width = 5, background = "black"
),
box(
title = "Gene expr", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot4"), #plotlyOutput
width = 5, background = "black"
),
box(
title = "Pheno - tSNE", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot3"), #plotlyOutput
width = 5, background = "black"
),
box(
title = "Pheno Legend", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot2"),
width = 5, background = "black"
),
box(
title = "Pos. Loadings GO", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("GOpos"), #plotlyOutput
width = 5, background = "black"
),
box(
title = "Neg. Loadings GO", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("GOneg"),
width = 5, background = "black"
),
box(
title = "Cell Scores Across", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("CellScoresAcross"),
width = 10, background = "black"
),
box(
title = "Chrom. Location", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("ChrLoc"),
width = 10, background = "black"
),
box(title = "Pos. Top Genes", status = "info", solidHeader = TRUE, width = 4, background = "black",
tableOutput("packageTablePos")
),
box(title = "Neg. Top Genes", status = "info", solidHeader = TRUE, width = 4, background = "black",
tableOutput("packageTableNeg")
)
),
##### tab2------------------
# Germ tab content
tabItem(tabName = "geneexprstatsig",
h2("Gene Expression Stat. Sig.")
,
fluidRow(
box(
title = "Inputs", status = "warning", solidHeader = TRUE,
#"Box content here", br(), "More box content",
#sliderInput("ComponentN", "Slider input:", 1, 150, 1),
width = 5,
textInput("Genetext_tab2", "Text input:", "CD69"),
radioButtons("metaselect_tab2", "Metadata Selection:",
c("EXP.ID" = "EXP.ID",
"SubjID" = "SubjectId",
"SampleDate" = "SampleDate",
#"Cluster" = "CustClus",
#"Cluster2" = "CustClus2",
"BarcodePrefix" = "BarcodePrefix",
"SingleR_Labels" = "SingleR_Labels",
"Unsup_Clust_BRtSNE" = "Unsup_Clust_BRtSNE"
)),
selectInput("clusres", "Clus res:",
c("0.001" = "0.001",
"0.005" = "0.005",
"0.01" = "0.01",
"0.05" = "0.05",
"0.1" = "0.1",
"0.5" = "0.5",
"0.8" = "0.8",
"1" = "1"), selected = "0.005"),
actionButton("ClustertSNEbr", "Cluster tSNEBr")
),
box(
title = "Gene Expression Stat. Sig. Meta", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("GeneExprSigMeta", height = 1000),
width = 10
)
)),
##### tab3------------------
# Soma tab content
tabItem(tabName = "somadash",
h2("Soma only tab content")
),
##### tab4------------------
# pseudotime tab content
tabItem(tabName = "pseudotime",
h2("Pseudotime tab content")
),
##### tab5------------------
# Enrichment tab content
tabItem(tabName = "Enrichment",
fluidRow(
box(
title = "Inputs", status = "warning", solidHeader = TRUE,
"Multiple formatting of gene sets accepted",
br(), "List can be seperated by comma e.g. from ",
br(), " or spaces e.g. from Excel",
br(), "Also, single or double quotes or not",
#sliderInput("ComponentN", "Slider input:", 1, 40, 1),
textInput("GeneSet", "A set of genes", "'GZMA', 'GZMK', 'MKI67'"),
width = 8
),
box(
title = "Positive Loadings", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot5"),
width = 10
),
box(
title = "Negative Loadings", status = "primary", solidHeader = TRUE,
collapsible = TRUE,
plotOutput("plot6"),
width = 10
)
)
)
##### end of tabs------------------
)
)
)
## Server ---------------------
server <- function(input, output, session) {
# col_vector <- OOSAP::ColorTheme()$col_vector
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
envv=reactiveValues(y=NULL)
### Input data -------------
# lookup.path <- "../../../Expts/TetCombo2/data"
observeEvent(input$SDAroot, {
envv$lookup.path = input$SDAroot
envv$SerObj.paths <- list.files(envv$lookup.path, full.names = F, recursive = T)[grepl('FinalSerObj', list.files(envv$lookup.path, recursive = T))]
})
observeEvent(input$compsIn, {
if(!is.null(envv$Keep_comps)){
if(input$compsIn == "TrueSigComps") {
envv$comps = as.numeric(envv$Keep_comps)
}
if(input$compsIn == "NoiseComps") {
envv$comps = as.numeric(envv$Remove_comps)
}
Val = as.character(min(envv$comps))
updateTextInput(session, "ComponentNtext", value = Val)
envv$comps <- sort(envv$comps)
Val = as.character(min(envv$comps))
envv$comps_order = 1
updateTextInput(session, "ComponentNtext", value = Val)
}
})
observeEvent(input$clusres, {
envv$cluster_res = as.numeric(input$clusres)
# cluster_res=0.01 # dropdown?
})
observeEvent(input$ClustertSNEbr, {
print("Finding Neibors on tSNE Batch-removed")
envv$SerObj.ShinySDAOut <- FindNeighbors(object = envv$SerObj.ShinySDAOut,
dims = c(1,2),
reduction = "tSNECSBR")
print("Finding clusters")
cluster_res=envv$cluster_res
envv$SerObj.ShinySDAOut <- FindClusters(object = envv$SerObj.ShinySDAOut, resolution = cluster_res)
# DimPlot(SerObj.ShinySDAOut, reduction = "tSNECSBR")
# table(Idents(envv$SerObj.ShinySDAOut))
envv$MetaDF$Clust_tSNECSBR <- paste0("Clust", as.character(Idents(envv$SerObj.ShinySDAOut)))
})
output$select.folder <-
renderUI(expr = selectInput(inputId = 'folder.name',
label = 'File Name',
choices = envv$SerObj.paths
))
## Load data --------------
observeEvent(input$loadSDA, {
# envv <- list()
# input <- list()
# input$SDAroot <- "../../../Expts/TetCombo3/data"
# input$folder.name <- "sda_results/Tet_SDADGE_DropSim_mi10000_nc40_N21509_rep1/Tet_SDADGE_DropSim_mi10000_nc40_N21509_rep1_FinalSerObj.rds"
envv$path2SDA_dyn <- paste0(input$SDAroot, "/", input$folder.name)
print("Loading SeuratSDAObj")
print(envv$path2SDA_dyn)
if(file.exists(envv$path2SDA_dyn)) {
SerObj.ShinySDAOut <- readRDS(envv$path2SDA_dyn)
print("SeuratSDAObj Loaded")
envv$SerObj.ShinySDAOut <- SerObj.ShinySDAOut
envv$CellScores <- SerObj.ShinySDAOut@[email protected]
envv$GeneLoadings <- t(SerObj.ShinySDAOut@[email protected])
envv$MetaDF <- SerObj.ShinySDAOut@misc$SDA_processing_results$MetaDF
# names(SerObj.ShinySDAOut@misc$SDA_processing_results)
# names(SerObj.ShinySDAOut@reductions$SDA@misc)
# SerObj.ShinySDAOut2 <- FindNeighbors(object = SerObj.ShinySDAOut,
# dims = as.numeric(SerObj.ShinySDAOut@misc$SDA_processing_results$Remove_comps),
# reduction = "SDA")
# SerObj.ShinySDAOut2 <- FindClusters(object = SerObj.ShinySDAOut2, resolution = 1.2)
# DimPlot(SerObj.ShinySDAOut2, reduction = "tSNECSBR")
#
# DimPlot(SerObj.ShinySDAOut2, reduction = "SDA")
print("Finding Neibors on tSNE Batch-removed")
SerObj.ShinySDAOut <- FindNeighbors(object = SerObj.ShinySDAOut,
dims = c(1,2),
reduction = "tSNECSBR")
print("Finding clusters")
cluster_res=envv$cluster_res
SerObj.ShinySDAOut <- FindClusters(object = SerObj.ShinySDAOut, resolution = cluster_res)
# DimPlot(SerObj.ShinySDAOut, reduction = "tSNECSBR")
table(Idents(SerObj.ShinySDAOut))
envv$MetaDF$Clust_tSNECSBR <- paste0("Clust", as.character(Idents(SerObj.ShinySDAOut)))
envv$GO_data <- SerObj.ShinySDAOut@misc$SDA_processing_results$GO_data
envv$chromosome.lengths <- SerObj.ShinySDAOut@misc$SDA_processing_results$chromosome.lengths
envv$gene_locations <- SerObj.ShinySDAOut@misc$SDA_processing_results$gene_locations
envv$Remove_comps <- as.numeric(SerObj.ShinySDAOut@misc$SDA_processing_results$Remove_comps)
envv$Keep_comps <- setdiff(1:ncol(envv$CellScores), envv$Remove_comps)
envv$SDA_TopNpos <- SerObj.ShinySDAOut@misc$SDA_processing_results$SDA_TopNpos
envv$SDA_TopNneg <- SerObj.ShinySDAOut@misc$SDA_processing_results$SDA_TopNneg
envv$TopN <- SerObj.ShinySDAOut@misc$SDA_processing_results$TopN
envv$MetaDF$tsne1_CS_raw <- SerObj.ShinySDAOut@misc$SDA_processing_results$tsne_CS_raw$Y[,1]
envv$MetaDF$tsne2_CS_raw <- SerObj.ShinySDAOut@misc$SDA_processing_results$tsne_CS_raw$Y[,2]
envv$MetaDF$tsne1_CS_br <- SerObj.ShinySDAOut@[email protected][,1]
envv$MetaDF$tsne2_CS_br <- SerObj.ShinySDAOut@[email protected][,2]
envv$StatFac <- data.frame(ord=1:ncol(envv$CellScores), Name=colnames(envv$CellScores), row.names = colnames(envv$CellScores))
envv$StatFac$Meta1 <- rep(0, nrow(envv$StatFac))
envv$StatFac$Meta2 <- rep(0, nrow(envv$StatFac))
envv$StatFac$Meta3 <- rep(0, nrow(envv$StatFac))
envv$StatFac$Meta4 <- rep(0, nrow(envv$StatFac))
envv$StatFac$Meta5 <- rep(0, nrow(envv$StatFac))
envv$StatFac$Meta6 <- rep(0, nrow(envv$StatFac))
if(input$compsIn == "TrueSigComps") {
envv$comps = as.numeric(envv$Keep_comps)
}
if(input$compsIn == "NoiseComps") {
envv$comps = as.numeric(envv$Remove_comps)
}
envv$comps <- sort(envv$comps)
Val = as.character(min(envv$comps))
envv$comps_order = 1
updateTextInput(session, "ComponentNtext", value = Val)
}
})
### Scores Across DT --------
ScoreAcrossDT <- reactive({
#these depend on metadata
if(!is.null(envv$MetaDF)){
# print(head(envv$MetaDF))
if(input$metaselect == "SubjectId") {
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$SubjectId))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(as.character(envv$MetaDF$SubjectId))]
# ColFac_DONR.ID
} else {
if(input$metaselect == "SampleDate"){
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$SampleDate))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(envv$MetaDF$SampleDate)]
} else {
if(input$metaselect == "BarcodePrefix"){
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$BarcodePrefix))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(envv$MetaDF$BarcodePrefix)]
} else {
if(input$metaselect == "SingleR_Labels"){
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$SingleR_Labels))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(envv$MetaDF$SingleR_Labels)]
} else {
if(input$metaselect == "Unsup_Clust_BRtSNE"){
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$Clust_tSNECSBR))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(envv$MetaDF$Clust_tSNECSBR)]
} else if(input$metaselect == "EXP.ID"){
ColFac_DONR.ID <- sort(as.character(envv$MetaDF$EXP.ID))
names(ColFac_DONR.ID) <- rownames(envv$MetaDF)[order(envv$MetaDF$EXP.ID)]
} else {
}
}
}
}
}
# print(ColFac_DONR.ID)
# input$ComponentNtext = 1
ComponentN <- as.numeric(input$ComponentNtext)
DTt <- data.frame(cell_index = 1:nrow(envv$CellScores),
score = envv$CellScores[, paste0("SDA_", ComponentN)],
experiment = gsub("_.*", "", gsub("[A-Z]+\\.", "", rownames(envv$CellScores))),
ColFac = as.character(ColFac_DONR.ID),
row.names = names(ColFac_DONR.ID))
# DTt$ColFac <- "unk"
# DTt[names(ColFac_DONR.ID),]$ColFac <- as.character(ColFac_DONR.ID)
DTt
}
})
### tSNE DF selection
tDF <- reactive({
if(!is.null(envv$MetaDF)){
if(input$data == "tsneCSraw") {
tempDF <- as.data.frame(envv$MetaDF)[, c("tsne1_CS_raw", "tsne2_CS_raw")]; colnames(tempDF) <- c("tSNE1", "tSNE2")
# tempDF
} else {
if(input$data == "tsneCSbr") {
tempDF <- as.data.frame(envv$MetaDF)[, c("tsne1_CS_br", "tsne2_CS_br")]; colnames(tempDF) <- c("tSNE1", "tSNE2")
} else {
}
# tempDF
}
rownames(tempDF) <- envv$MetaDF$barcode
tempDF$GeneExpr <- rep(0, nrow(tempDF))
return(tempDF)
}
})
observeEvent(input$NextSDA, {
if (envv$comps_order + 1 %in% 1:length(envv$comps)){
envv$comps_order <- envv$comps_order + 1
}
Val <- as.character(envv$comps[envv$comps_order])
updateTextInput(session, "ComponentNtext", value = Val)
})
observeEvent(input$PrevSDA, {
if (envv$comps_order - 1 %in% 1:length(envv$comps)){
envv$comps_order <- envv$comps_order - 1
}
Val <- as.character(envv$comps[envv$comps_order])
updateTextInput(session, "ComponentNtext", value = Val)
})
observeEvent(input$C2Cpos, {
Out1 <- print_gene_list(as.numeric(input$ComponentNtext), PosOnly = T, GeneLoadings = envv$GeneLoadings) %>%
as.data.frame() %>%
head(as.numeric(input$NoOfGenes))
Out1 <- Out1$Gene.Name
clipr::write_clip(Out1)
})
observeEvent(input$C2Cneg, {
Out2 <- print_gene_list(as.numeric(input$ComponentNtext), NegOnly = T, GeneLoadings = envv$GeneLoadings) %>%
as.data.frame() %>%
head(as.numeric(input$NoOfGenes))
Out2 <- Out2$Gene.Name
clipr::write_clip(Out2)
})
output$packageTablePos <- renderTable({
if(!is.null(envv$GeneLoadings)){
print_gene_list(as.numeric(input$ComponentNtext), PosOnly = T, GeneLoadings = envv$GeneLoadings) %>%
as.data.frame() %>%
head(as.numeric(input$NoOfGenes))
}
}, digits = 1)
output$packageTableNeg <- renderTable({
if(!is.null(envv$GeneLoadings)){
print_gene_list(as.numeric(input$ComponentNtext), NegOnly = T, GeneLoadings = envv$GeneLoadings) %>%
as.data.frame() %>%
head(as.numeric(input$NoOfGenes))
}
}, digits = 1)
output$cellinfo1 <- renderValueBox({
valueBox(
value = envv$StatFac[paste0("SDA_", input$ComponentNtext, sep=""),2], #format(Sys.time(), "%a %b %d %X %Y %Z"),
subtitle = envv$StatFac[paste0("SDA_", input$ComponentNtext, sep=""),6],
icon = icon("area-chart"),
color = "yellow" #if (downloadRate >= input$rateThreshold) "yellow" else "aqua"
)
})
output$GeneName <- renderValueBox({
if(input$Genetext %in% colnames(envv$GeneLoadings)){
# envv$GeneLoadings[,"PRM1"]
GeneName <- input$Genetext
} else {
GeneName <- paste0(input$Genetext, " Not Found")
}
valueBox(
value = GeneName,
subtitle = "Gene Name",
icon = icon("area-chart"),
color = "yellow"
)
})
#renderPlotly
output$plot1 <- renderPlot({
if(!is.null(envv$CellScores)){
#ggplotly
tempDF <- tDF()
(ggplot(cbind(tempDF, SDAComp=envv$CellScores[,paste0("SDA_", input$ComponentNtext, sep="")]),
aes(tSNE1, tSNE2, color=cut(asinh(SDAComp), breaks = c(-Inf, -1, -.5, 0, .5, 1, Inf)))) +
geom_point(size=0.1) + theme_bw() +
scale_color_manual("CS", values = rev(c("red", "orange", "yellow", "lightblue", "dodgerblue", "blue")) ) +
guides(colour = guide_legend(override.aes = list(size=2, alpha=1))) +
theme(legend.position = "bottom", aspect.ratio=1,
panel.background = element_rect(fill = "black",
colour = "black",
size = 0.5, linetype = "solid")) +
ggtitle(paste0("SDA_", input$ComponentNtext, " \n",
envv$StatFac[paste0("SDA_", input$ComponentNtext, sep=""),2], sep="")) +
simplify2 + coord_cartesian(xlim = NULL, ylim = NULL, expand = FALSE))
}
})
output$plot2 <- renderPlot({
if(!is.null(envv$MetaDF)){
tempDF <- tDF()
if(input$metaselect == "SubjectId") {
MetaFac <- (envv$MetaDF$SubjectId)
} else {
if(input$metaselect == "SampleDate"){
MetaFac <- (envv$MetaDF$SampleDate)
} else {
if(input$metaselect == "BarcodePrefix"){
MetaFac <- (envv$MetaDF$BarcodePrefix)
} else {
if(input$metaselect == "SingleR_Labels"){
MetaFac <- (envv$MetaDF$SingleR_Labels)
} else {
if(input$metaselect == "Unsup_Clust_BRtSNE"){
MetaFac <- (envv$MetaDF$Clust_tSNECSBR)
} else if(input$metaselect == "EXP.ID"){
MetaFac <- (envv$MetaDF$EXP.ID)
} else {
}
}
}
}
}
ggFph <- ggplot(tempDF, aes(tSNE1, tSNE2, color=factor(MetaFac))) +
geom_point(size=0.1)+ theme_bw() +
theme(legend.position = "bottom", aspect.ratio=1,
legend.title = element_blank()) +
ggtitle("t-SNE - Final Pheno") +
scale_color_manual(values=rev(col_vector)) + guides(colour = guide_legend(override.aes = list(size=2, alpha=1), ncol =2)) +
coord_cartesian(xlim = NULL, ylim = NULL, expand = FALSE)
legend <- cowplot::get_legend(ggFph)
#grid.newpage()
grid.draw(legend)
}
})
#renderPlotly
output$plot3 <- renderPlot({
if(!is.null(envv$GeneLoadings)){
tempDF <- as.data.frame(tDF())
if(input$metaselect == "SubjectId") {
MetaFac <- (envv$MetaDF$SubjectId)
} else {
if(input$metaselect == "SampleDate"){
MetaFac <- (envv$MetaDF$SampleDate)
} else {
if(input$metaselect == "BarcodePrefix"){
MetaFac <- (envv$MetaDF$BarcodePrefix)
} else {
if(input$metaselect == "SingleR_Labels"){
MetaFac <- (envv$MetaDF$SingleR_Labels)
} else {
if(input$metaselect == "Unsup_Clust_BRtSNE"){
MetaFac <- (envv$MetaDF$Clust_tSNECSBR)
} else if(input$metaselect == "EXP.ID"){
MetaFac <- (envv$MetaDF$EXP.ID)
} else {
}
}
}
}
}
#ggplotly
(ggplot(tempDF, aes(tSNE1, tSNE2, color=factor(MetaFac))) +
geom_point(size=0.1)+ theme_bw() +
theme(legend.position = "none", aspect.ratio=1,
panel.background = element_rect(fill = "black",
colour = "black",
size = 0.5, linetype = "solid")) +
ggtitle("t-SNE - Final Pheno") +
scale_color_manual(values=rev(col_vector)) +
coord_cartesian(xlim = NULL, ylim = NULL, expand = FALSE))
}
})
output$plot4 <- renderPlot({
if(!is.null(envv$GeneLoadings)){
tempDF <- as.data.frame(tDF())
if(input$Genetext %in% colnames(envv$GeneLoadings)){
# envv$GeneLoadings[,"PRM1"]
GeneExpr <- envv$CellScores %*% envv$GeneLoadings[,as.character(input$Genetext)]
} else {
GeneExpr <- envv$CellScores %*% rep(0, nrow(envv$GeneLoadings))
}
#ggplotly
LoadOrdVal <- round(envv$GeneLoadings[,as.character(input$Genetext)][order(abs(envv$GeneLoadings[,as.character(input$Genetext)]), decreasing = T)], 3)
tempDF[rownames(GeneExpr), ]$GeneExpr <- GeneExpr[,1]
(ggplot(tempDF,
aes(tSNE1, tSNE2, color=cut(asinh(GeneExpr), breaks = c(-Inf, -1, -.5, 0, .5, 1, Inf)))) +
geom_point(size=0.1) + theme_bw() +
scale_color_manual("EX", values = rev(c("red", "orange", "yellow", "lightblue", "dodgerblue", "blue")) ) +
guides(colour = guide_legend(override.aes = list(size=2, alpha=1))) +
theme(legend.position = "bottom", aspect.ratio=1,
panel.background = element_rect(fill = "black",
colour = "black",
size = 0.5, linetype = "solid")) +
simplify2 +
coord_cartesian(xlim = NULL, ylim = NULL, expand = FALSE)) +
labs(title = paste("Gene: ", input$Genetext, sep=""),
subtitle = paste("Found in comps: \n",
paste(names(LoadOrdVal)[1:5], collapse = ", "),
"\n",
paste(LoadOrdVal[1:5], collapse = ", "),
"\n",
paste(names(LoadOrdVal)[6:10], collapse = ", "),
"\n",
paste(LoadOrdVal[6:10], collapse = ", "),
"\n"),
caption = "Caption here")
}
})
output$GOpos <- renderPlot({
if(!is.null(envv$GO_data)){
if(! (as.numeric(input$ComponentNtext) %in% envv$comps)){
print("No GO")
} else {
go_volcano_plot(x=envv$GO_data, component = paste("V", input$ComponentNtext, "P", sep=""))+ theme_bw()+ theme(aspect.ratio = 1)
}
}
})
output$GOneg <- renderPlot({
if(!is.null(envv$GO_data)){
if(! (as.numeric(input$ComponentNtext) %in% envv$comps)){
print("No GO")
} else {
go_volcano_plot(x=envv$GO_data, component = paste("V", input$ComponentNtext, "N", sep=""))+ theme_bw()+ theme(aspect.ratio = 1)
}
}
})
output$ChrLoc <- renderPlot({
if(!is.null(envv$GeneLoadings)){
if(! (as.numeric(input$ComponentNtext) %in% envv$comps)){
print("No Comp")
} else {
pgl <- genome_loadings(envv$GeneLoadings[as.numeric(input$ComponentNtext),],
label_both = T,
max.items = 10,
gene_locations = envv$gene_locations,
chromosome_lengths = envv$chromosome.lengths, GeneLoadings = envv$GeneLoadings)+ theme(aspect.ratio = .5)
print(pgl)
}
}
})
output$CellScoresAcross <- renderPlot({
if(!is.null(envv$CellScores)){
DTt <- ScoreAcrossDT()
ComponentN <- as.numeric(input$ComponentNtext)
# envv$CellScores <- envv$CellScores
if(! (as.numeric(input$ComponentNtext) %in% envv$comps)){
print("No Comp")