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server.R
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shinyServer(function(input, output, session){
####First Page inputs/outputs####
options(shiny.maxRequestSize=10000*1024^2) #10 GB
dataGeneList <- reactive({
infileGene <- input$geneList
if (is.null(infileGene)){
return(NULL)
}
dGL <- as.data.frame(read.csv(input$geneList$datapath,
header = input$headerGeneList,
sep = ",",
quote = '"'))
return(dGL)
})
dataClusterList <- reactive({
infileCluster <- input$clusterList
if (is.null(infileCluster)) {
return(NULL)
}
dCL <- as.data.frame(read.csv(input$clusterList$datapath,
header = input$headerClusterList,
sep = ",",
quote = '"'))
if (ncol(dCL) > 1) {
dCL <- as.data.frame(do.call(paste, c(dCL, sep = " ")))
}
return(dCL)
})
output$tableGeneList <- renderTable({
dataGeneList()
},bordered = TRUE)
output$tableClusterList <- renderTable({
dataClusterList()
},bordered = TRUE)
dataExpression <- reactive({
req(input$expression)
if(input$transposeExpression == TRUE){
dfExpression <- as.data.frame(t(fread(input$expression$datapath,
header = "auto",
sep = input$sepExpression,
quote = input$quoteExpression)))
} else {
dfExpression <- fread(input$expression$datapath,
header = "auto",
sep = input$sepExpression,
quote = input$quoteExpression,
data.table = FALSE)
}
row.names(dfExpression) <- dfExpression[,1]
dfExpression[,1] <- NULL
#dfExpressionNew <- data.frame(dfExpression[,-1],row.names = dfExpression[,1])
dfExpressionNew <- dfExpression
if(!is.null(dataClusterList())){
sampleClusterIndex <- which(dataClusterUpload()[,1] %in% dataClusterList()[,1])
dfExpressionNew <- dfExpressionNew[,sampleClusterIndex]
}
# dfNames <- row.names(dfExpressionNew)
# updateSelectizeInput(session, 'name',choices = sort(dfNames), server = TRUE)
return(round(dfExpressionNew,2))
})
dataExpressionGeneList <- reactive({
if(!is.null(dataGeneList())){
sampleGeneIndex <- which(rownames(dataExpression()) %in% dataGeneList()[,1])
dfExpressionNew <- dataExpression()[sampleGeneIndex,]
}
return(dfExpressionNew)
})
observe({
input$updateNameList
if(input$updateNameList == TRUE && !is.null(dataGeneList()) == TRUE) {
dfNames <- row.names(dataExpressionGeneList())
} else {
dfNames <- row.names(dataExpression())
}
updateSelectizeInput(session, 'name',choices = sort(dfNames), server = TRUE)
})
dataClusterUpload <- reactive({
req(input$cluster)
if(input$transposeCluster == TRUE){
dfClusterUpload <- as.data.frame(t(read.csv(input$cluster$datapath,
header = input$headerCluster,
sep = input$sepCluster,
quote = input$quoteCluster)))
} else {
dfClusterUpload <- as.data.frame(read.csv(input$cluster$datapath,
header = input$headerCluster,
sep = input$sepCluster,
quote = input$quoteCluster))
}
if (ncol(dfClusterUpload) > 1) {
dfClusterUpload <- as.data.frame(do.call(paste, c(dfClusterUpload, sep = " ")))
}
colnames(dfClusterUpload) <- c("ClusterNames")
return(dfClusterUpload)
})
dataCluster <- reactive({
dfCluster <- dataClusterUpload()
if(!is.null(dataClusterList())){
sampleClusterIndex <- which(dfCluster[,1] %in% dataClusterList()[,1])
dfCluster <- as.data.frame(dfCluster[sampleClusterIndex,])
}
colnames(dfCluster) <- c("ClusterNames")
return(as.data.frame(dfCluster))
})
dataClusterNames <- reactive({
CN <- as.data.frame(unique(dataCluster()))
colnames(CN) <- c("UniqueClusterNames")
return(CN)
})
output$tableExpression <- renderTable({
dataExpression()[1:5,1:5]
},rownames = TRUE,bordered = TRUE)
output$tableCluster <- renderTable({
dataCluster <- as.data.frame(dataCluster())
head(dataCluster)
},bordered = TRUE)
output$tableClusterNames <- renderTable({
dataClusterNames()
},bordered = TRUE)
clusterPalette <- reactive({
brewer.pal(max(dim(dataClusterNames())),"Set3")
})
dataGeneExpressionCluster <- reactive({
namesExpression <- row.names(dataExpression())
gene_idx <- as.numeric(which(namesExpression==input$name))
dataGeneExpression_t <- as.data.frame(t(dataExpression()[gene_idx,]))
dataCluster_new <- dataCluster()
dataGeneExpression_t$Cluster <- dataCluster_new$ClusterNames
colnames(dataGeneExpression_t) <- c('Gene','Cluster')
return(dataGeneExpression_t)
})
output$tableClusterDataFull <- DT::renderDataTable({
DT::datatable(dataGeneExpressionCluster(),options = list(pageLength = 20))
})
output$tableClusterDataPos <- DT::renderDataTable({
it <- thresholdCheck(input$threshold)
dat_pos <-dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,]
DT::datatable(dat_pos,options = list(pageLength = 20))
})
output$noGenes <- renderTable({
noGenesTable <- matrix(0,1,1)
colnames(noGenesTable) <- "No. of Genes"
noGenesTable[1,1] <- dim(dataExpression())[1]
return(noGenesTable)
}, digits = 0, bordered = T)
output$noCells <- renderTable({
noCellsTable <- matrix(0,1,1)
colnames(noCellsTable) <- "No. of Cells"
noCellsTable[1,1] <- dim(dataExpression())[2]
return(noCellsTable)
}, digits = 0, bordered = T)
output$noClusters <- renderTable({
noClustersTable <- matrix(0,1,1)
colnames(noClustersTable) <- "No. of Cells with Cluster Info."
noClustersTable[1,1] <- dim(dataCluster())[1]
return(noClustersTable)
}, digits = 0, bordered = T)
output$noUnique <- renderTable({
noUniqueTable <- matrix(0,1,1)
colnames(noUniqueTable) <- "No. of Unique Clusters"
noUniqueTable[1,1] <- dim(dataClusterNames())[1]
return(noUniqueTable)
}, digits = 0, bordered = T)
output$noList <- renderTable({
noListTable <- matrix(0,1,1)
colnames(noListTable) <- "No. of Genes in List"
noListTable <- dim(dataGeneList())[1]
return(noListTable)
}, digits = 0, bordered = T)
####TestCases####
output$checkNo <- reactive({
as.character(isTRUE(dim(dataCluster())[1] == dim(dataExpression())[2]))
})
output$conditionDataEntry <- reactive({
as.character(class(dataExpression()) == "data.frame" & class(dataClusterUpload()) == "data.frame")
})
output$conditionNameEntry <- reactive({
as.character(any(input$name == row.names(dataExpression())))
})
output$conditionListEntry <- reactive({
as.character(class(dataGeneList()) == "data.frame")
})
####DensityPlots####
densityPositive <- reactive({
palette <- clusterPalette()
it <- thresholdCheck(input$threshold)
dataDensityPositive <- as.data.frame(dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,])
p <- ggplot(dataDensityPositive, aes(log2(Gene+1), fill=factor(Cluster))) +
geom_density(aes( y=..scaled.. ), alpha=0.5) +
theme_bw() + ylab('Scaled Density') + xlab('log2(Expression+1)') +
scale_fill_manual(name="SubPop", values=c(palette),limits=t(dataClusterNames())) +
labs(title = paste0("Gene Expression of Positive Cells:\n", input$name)) +
theme(plot.title = element_text(hjust = 0.5))
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(p) %>% layout(margin = m)
p
})
output$densityPositive <- renderPlotly(densityPositive())
densityFull <- reactive({
palette <- clusterPalette()
dataDensityFull <- as.data.frame(dataGeneExpressionCluster())
p <- ggplot(dataDensityFull, aes(log2(Gene+1), fill=factor(Cluster))) +
geom_density(aes( y=..scaled.. ), alpha=0.5) +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
ylab('Scaled Density') + xlab('log2(Expression+1)') +
scale_fill_manual(name="SubPop", values=c(palette),limits=t(dataClusterNames())) +
ggtitle(paste0("Gene Expression of All Cells:\n", input$name))
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(p) %>% layout(margin = m)
p
})
output$densityFull <- renderPlotly(densityFull())
densityPositiveUnscaled <- reactive({
palette <- clusterPalette()
it <- thresholdCheck(input$threshold)
dataDensityPositive <- as.data.frame(dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,])
p <- ggplot(dataDensityPositive, aes(log2(Gene+1), fill=as.factor(Cluster))) +
geom_density(alpha=0.5) +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
ylab('Density') + xlab('log2(Expression+1)') +
scale_fill_manual(name="SubPop", values=c(palette),limits=t(dataClusterNames())) +
ggtitle(paste0("Gene Expression of Positive Cells:\n", input$name))
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(p) %>% layout(margin = m)
p
})
output$densityPositiveUnscaled <- renderPlotly(densityPositiveUnscaled())
densityFullUnscaled <- reactive({
palette <- clusterPalette()
dataDensityFull <- dataGeneExpressionCluster()
p <- ggplot(dataDensityFull, aes(log2(Gene+1), fill = factor(Cluster))) +
geom_density(alpha=0.5)+
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
ylab('Density') + xlab('log2(Expression+1)') +
scale_fill_manual(name="SubPop", values=c(palette),limits=t(dataClusterNames())) +
ggtitle(paste0("Gene Expression of All Cells:\n", input$name))
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(p) %>% layout(margin = m)
p
})
output$densityFullUnscaled <- renderPlotly(densityFullUnscaled())
####SummaryData####
SData <- reactive({
SD <- as.data.frame(matrix(0,5,max(dim(dataClusterNames()))))
it <- thresholdCheck(input$threshold)
for (i in 1:max(dim(dataClusterNames()))) {
dCN <- as.character(dataClusterNames()[i,1])
summary_output <- summary_data_big(dCN, dataGeneExpressionCluster(),it)
SD[1,i] <- summary_output[[1]]
SD[2,i] <- summary_output[[2]]
SD[3,i] <- summary_output[[3]]
SD[4,i] <- summary_output[[4]]
SD[5,i] <- summary_output[[5]]
colnames(SD)[i] <- paste0(dCN)
}
rownames(SD) <- c("Cell Count", "Positive Cells", "Percent Positive Cells", "Mean Expr. All", "Mean Expr. Positive")
return(SD)
})
output$summaryData <- renderTable({
SData()
},rownames = T, digits = 2)
####SunburstPlot####
SBdf <- reactive({
SB <- as.data.frame(matrix(0,2*max(dim(dataClusterNames())),4))
it <- thresholdCheck(input$threshold)
for (i in 1:max(dim(dataClusterNames()))) {
dCN <- as.character(dataClusterNames()[i,1])
SB[2*i-1,1] <- "Total"
SB[2*i,1] <- "Total"
SB[2*i-1,2] <- paste0(dCN)
SB[2*i,2] <- paste0(dCN)
SB[2*i-1,3] <- "On"
SB[2*i,3] <- "Off"
sunburst_output <- summary_data_sunburst(dCN, dataGeneExpressionCluster(),it)
SB[2*i-1,4] <- as.integer(sunburst_output[[2]])
SB[2*i,4] <- as.integer(sunburst_output[[1]]-sunburst_output[[2]])
}
colnames(SB) <- c('DataSet','Cluster','State','Count')
return(SB)
})
output$sunburstData <- renderTable({
SBdf()
})
output$sunburstPlot <- renderSunburst({
tmSB <- treemap(SBdf(), index=c("DataSet","Cluster","State"), vSize="Count",
vColor="State", type="index")
tmSB_new <- tmSB$tm
tmSB_new$vSize[which(is.na(tmSB_new$State))] <- 0
tmSB_new$color[which(tmSB_new$DataSet == 'Total')] <- '#00a6a7'
for (i in seq_len(nrow(dataClusterNames()))) {
dCN <- as.character(dataClusterNames()[i,1])
tmSB_new$color[which(tmSB_new$Cluster == paste0(dCN))] <- clusterPalette()[i]
}
tmSB_new$color[which(tmSB_new$State == 'On')] <- '#f5001a'
tmSB_new$color[which(tmSB_new$State == 'Off')] <- '#d5d4d2'
tmSB_nest <- d3_nest(
tmSB_new[,c("DataSet","Cluster","State", "vSize", "color")],
value_cols = c("vSize", "color"))
sunburst(data = tmSB_nest, valueField = "vSize", count = TRUE,
colors = htmlwidgets::JS("function(d){return d3.select(this).datum().data.color;}"),
withD3 = TRUE
)
})
selection <- reactive({
input$sunburst_mouseover
})
output$selection <- renderText(selection())
####KS Tables####
ksTableFull <- reactive({
n <- nrow(dataClusterNames())
sampleInd <- combn(1:n,2)
sampleNames <- combn(t(dataClusterNames()),2)
ksTable <- matrix(NA,n,n+1)
dat_full <- dataGeneExpressionCluster()
for (i in seq_len(ncol(sampleInd))){
m = length(dat_full[dat_full$Cluster==sampleNames[1,i],]$Gene)
k = length(dat_full[dat_full$Cluster==sampleNames[2,i],]$Gene)
if (m<3 | k<3){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- NA
ksTable[sampleInd[2,i],sampleInd[1,i]] <- NA
} else {
sampleKS <- ks.test(dat_full[dat_full$Cluster==sampleNames[1,i],]$Gene,
dat_full[dat_full$Cluster==sampleNames[2,i],]$Gene)
if (input$ksTypeFull == "P"){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- sampleKS$p.value
ksTable[sampleInd[2,i],sampleInd[1,i]] <- sampleKS$p.value
} else if (input$ksTypeFull == "T"){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- sqrt((m*k)/(m+k))*sampleKS$statistic
ksTable[sampleInd[2,i],sampleInd[1,i]] <- sqrt((m*k)/(m+k))*sampleKS$statistic
}
}
}
for (i in 1:n){
ksTable[i,ncol(ksTable)] <- length(dat_full[dat_full$Cluster==as.character(dataClusterNames()[i,1]),]$Gene)
}
colnames(ksTable) <- c(t(dataClusterNames()),"Count")
rownames(ksTable) <- t(dataClusterNames())
return((ksTable))
})
output$ksTableFull <- renderTable({
ksTableFull()[seq_len(nrow(dataClusterNames())), seq_len(nrow(dataClusterNames()))]
},spacing = "m",align = 'r',rownames = TRUE, digits = -1,na = "",bordered = TRUE)
output$ksTableFullCount <- renderTable({
trender <- as.data.frame(ksTableFull()[,ncol(ksTableFull())])
colnames(trender) <- c("Counts")
return(trender)
},spacing = "l",align = 'r',rownames = TRUE,digits = 0,na = "",bordered = TRUE)
ksTablePositive <- reactive({
n <- nrow(dataClusterNames())
sampleInd <- combn(seq_len(n), 2)
sampleNames <- combn(t(dataClusterNames()),2)
ksTable <- matrix(NA,n,n+1)
it <- thresholdCheck(input$threshold)
dat_pos <-dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,]
for (i in 1:dim(sampleInd)[2]){
m <- length(dat_pos[dat_pos$Cluster==sampleNames[1,i],]$Gene)
k <- length(dat_pos[dat_pos$Cluster==sampleNames[2,i],]$Gene)
if (m<3 | k<3){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- NA
ksTable[sampleInd[2,i],sampleInd[1,i]] <- NA
} else {
sampleKS <- ks.test(dat_pos[dat_pos$Cluster==sampleNames[1,i],]$Gene,
dat_pos[dat_pos$Cluster==sampleNames[2,i],]$Gene)
if (input$ksTypePositive == "P"){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- sampleKS$p.value
ksTable[sampleInd[2,i],sampleInd[1,i]] <- sampleKS$p.value
} else if(input$ksTypePositive == "T"){
ksTable[sampleInd[1,i],sampleInd[2,i]] <- sqrt((m*k)/(m+k))*sampleKS$statistic
ksTable[sampleInd[2,i],sampleInd[1,i]] <- sqrt((m*k)/(m+k))*sampleKS$statistic
}
}
}
it <- thresholdCheck(input$threshold)
for (i in 1:n){
ksTable[i,ncol(ksTable)] <- length(dat_pos[dat_pos$Cluster==as.character(dataClusterNames()[i,1]),]$Gene>it)
}
colnames(ksTable) <- c(t(dataClusterNames()), "Count")
rownames(ksTable) <- t(dataClusterNames())
return(ksTable)
})
output$ksTablePositive <- renderTable({
ksTablePositive()[seq_len(nrow(dataClusterNames())), seq_len(nrow(dataClusterNames()))]
},spacing = "m",align = 'r',rownames = TRUE,digits = -1,na = "",bordered = TRUE)
output$ksTablePositiveCount <- renderTable({
trender <- as.data.frame(ksTablePositive()[,ncol(ksTablePositive())])
colnames(trender) <- c("Counts")
return(trender)
},spacing = "l",align = 'r',rownames = TRUE,digits = 0,na = "",bordered = TRUE)
####KW Tables####
kwTableFull <- reactive({
kruskalData <- kruskal.test(dataGeneExpressionCluster()$Gene ~ dataGeneExpressionCluster()$Cluster)
kw <- matrix(0,1,3)
kw[1,] <- c(kruskalData$p.value,kruskalData$statistic,kruskalData$parameter)
colnames(kw) <- c("p.value","Chi-squared","Df")
return(kw)
})
output$kwTableFull <- renderTable({
t(kwTableFull()[1,1])
},spacing = "m",align = 'r',digits = -1,bordered = TRUE)
output$kwTableFullOther <- renderTable({
t(kwTableFull()[1,2:3])
},spacing = "m",align = 'r',bordered = TRUE)
kwTablePos <- reactive({
it <- thresholdCheck(input$threshold)
dat_pos <-dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,]
kruskalData <- kruskal.test(dat_pos$Gene ~ dat_pos$Cluster)
kw <- matrix(0,1,3)
kw[1,] <- c(kruskalData$p.value,kruskalData$statistic,kruskalData$parameter)
colnames(kw) <- c("p.value","Chi-squared","Df")
return(kw)
})
output$kwTablePos <- renderTable({
t(kwTablePos()[1,1])
},spacing = "m",align = 'r',digits = -1,bordered = TRUE)
output$kwTablePosOther <- renderTable({
t(kwTablePos()[1,2:3])
},spacing = "m",align = 'r',bordered = TRUE)
output$heatmap <- renderPlotly({
if (input$clusterOrderType == 'G') {
#init <- Sys.time()
maExpression <- as.matrix(dataExpression())
maDCN <- as.matrix(dataClusterNames())
DE <- matrix(0,nrow(maExpression),nrow(maDCN))
rownames(DE) <- rownames(maExpression)
for (i in 1:nrow(maDCN)) { # Use apply
DE[,i] <- rowMeans(maExpression[,dataCluster() == maDCN[i,1]])
}
DE <- as.data.frame(DE)
#print(Sys.time() - init)
} else {
DE <- dataExpression()
}
if (input$geneOrderType == 'VM') {
dfExpressionOrdered <- DE[order(as.data.frame(apply(DE, 1, varMean))),] # rowMeans
heatTitle <- c("Gene Expression Heatmap<br>Ascending Variance/Mean Ordered")
} else if (input$geneOrderType == 'M') {
dfExpressionOrdered <- DE[order(as.data.frame(rowMeans(DE))),] # rowMeans
heatTitle <- c("Gene Expression Heatmap<br>Ascending Mean Ordered")
} else if (input$geneOrderType == "V") {
dfExpressionOrdered <- DE[order(as.data.frame(apply(DE, 1, var))),]
heatTitle <- c("Gene Expression Heatmap<br>Ascending Variance Ordered")
} else {
dfExpressionOrdered <- as.data.frame(DE)
heatTitle <- c("Gene Expression Heatmap<br> ")
}
if (is.null(dataGeneList())) {
numRow <- nrow(dfExpressionOrdered)
dfExpressionOrdered <- dfExpressionOrdered[(numRow-51):numRow,]
plotHeight <- as.character(870)
} else {
plotHeight <- as.character(120+nrow(dfExpressionOrdered)*15)
}
genenames.text <- rep.col(rownames(dfExpressionOrdered), ncol(dfExpressionOrdered))
if (input$clusterOrderType == 'G') {
clusternames.text <- rep.row(maDCN, nrow(dfExpressionOrdered))
} else {
clusternames.text <- rep.row(as.matrix(dataCluster()), nrow(dfExpressionOrdered))
}
combined.text <- matrix(paste('Gene: ', genenames.text,
'<br> Cluster: ', clusternames.text,
'<br> Expression: ', as.matrix(dfExpressionOrdered)),
nrow(dfExpressionOrdered), ncol(dfExpressionOrdered))
h <- plot_ly(y = rownames(dfExpressionOrdered),
z = log2(as.matrix(dfExpressionOrdered)+1),
type = "heatmap",
colors = colorRamp(c("#E5E5E5", "#FF0000")),
hoverinfo = 'text',
text = combined.text) %>%
layout(
title = heatTitle,
height = plotHeight, autosize = TRUE,
margin = list(l = 250,r = 0,b = 25,t = 75,pad = 0))
h
})
output$tableMeanVar <- DT::renderDataTable({
dat <- dataExpression()
tableMeanVar <- NULL
#tableMeanVar$geneNames <- dat[,1]
tableMeanVar$geneMean <- apply(dat[,2:ncol(dat)],1,mean)
tableMeanVar$geneVar <- apply(dat[,2:ncol(dat)],1,var)
tableMeanVardf <- as.data.frame(tableMeanVar)
DT::datatable(tableMeanVardf[order(-tableMeanVardf$geneMean),],options = list(pageLength = 20))
})
libSizeExprsGenesFull <- reactive({
palette <- clusterPalette()
expression.matrix <- as.data.frame(dataExpression())
cell.information <- dataGeneExpressionCluster()$Cluster
batch.names <- unique(cell.information)
## Subset the data
total.counts <- colSums(expression.matrix)
total.features <- apply(expression.matrix, 2, function(x){sum(x>0)})
## Tidy dataframe
combined.data <- list(TotalCounts = total.counts, FeatureCounts = total.features, BatchInfo = cell.information)
combined.df <- as.data.frame(combined.data)
# ggplot
gradient.ramp <- ggplot2::scale_colour_gradient(low="#001b7f", high="#f1d351")
control.plot <- ggplot2::ggplot(combined.df, ggplot2::aes(x = factor(BatchInfo), y = TotalCounts, colour = FeatureCounts))
control.plot <- control.plot + ggplot2::geom_violin(size = 1, scale = "width", colour = NA) + ggbeeswarm::geom_quasirandom(shape = 16, size=5, alpha=0.5, dodge.width=0.5, groupOnX = T)
control.plot <- control.plot + gradient.ramp + ggplot2::scale_x_discrete(limits = batch.names) + ggplot2::xlab("Sample") + ggplot2::ylab("Total mapped reads per cell")
control.plot <- control.plot + ggplot2::ggtitle("Total mapped reads and genes per sample")
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(control.plot) %>% layout(margin = m)
p
})
output$heatmap2 <- renderPlotly({
DE <- dataExpressionGeneList()
#totalReadsPerGenes <- rowSums(DE)
DE <- log2(DE+1)
k_row_input <-input$heatmap2Input
if (input$Scaling == 'N') {
# p <- heatmaply(DE, k_row = k_row_input, labCol = NA, Colv = NULL) #, scale="row"
p <- heatmaply(DE, k_row = k_row_input, Colv = NULL, showticklabels = c(F,T),
label_names = c('Gene','Cell','Exprs'),labRow = rownames(DE),labCol = colnames(DE),
margins = c(0,250,0,0), colors = c('grey90','red'))#,scale = "row")
} else {
DE <- scale(DE, center = TRUE, scale = TRUE)
#p <- heatmaply(DE, k_row = k_row_input, labCol = NA, Colv = NULL, scale="row")
p <- heatmaply(DE, k_row = k_row_input, Colv = NULL, showticklabels = c(F,T),
label_names = c('Gene','Cell','Exprs'),labRow = rownames(DE),labCol = colnames(DE),
margins = c(0,250,0,0), colors = c('grey90','red'))#,scale = "row")
}
plotHeight <- as.character(120+nrow(dataExpressionGeneList())*15)
p2 <- p %>% layout(height = plotHeight, autosize = TRUE,
margin = list(l = 250,r = 0,b = 25,t = 75,pad = 0)
)
p2
})
#Gene list analysis-------------------------------------------------------------
output$reactome <- renderForceNetwork({
Genes <- dataGeneList()[,1]
Genes <- gsub("_.*","",Genes)
convert_to_gene_ID = bitr(Genes, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")
converted_genes = convert_to_gene_ID$ENTREZID
#totalReadsPerGenes <- rowSums(DE)
p_cutoff <-input$p_cutoff
x <- enrichPathway(gene=converted_genes,pvalueCutoff= p_cutoff, readable=TRUE)
#p <- enrichMap(x, layout=igraph::layout.kamada.kawai, vertex.label.cex = 1)
p <- emapplot.enrichResult(x)
k <- igraph_to_networkD3(p)
k$nodes$group <- as.factor(matrix(1,length(k$nodes$name),1))
iD3 <- forceNetwork(Links = k$links, Nodes = k$nodes,Source = "source", Target = "target",
Value = "value", NodeID = "name", Group = "group",
linkDistance = JS("function(d){return d.value * 15}"),
fontSize = 18, charge = -20, bounded = TRUE) #,linkColour = "FF530D"
return(iD3)
#return(list("iD3"=iD3))# "p_cnet" = p_cnet))
})
output$cnet <- renderPlot({
Genes <- dataGeneList()[,1]
Genes <- gsub("_.*","",Genes)
convert_to_gene_ID = bitr(Genes, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")
converted_genes = convert_to_gene_ID$ENTREZID
#totalReadsPerGenes <- rowSums(DE)
p_cutoff <-input$p_cutoff
x <- enrichPathway(gene=converted_genes,pvalueCutoff= p_cutoff, readable=TRUE)
<<<<<<< HEAD
p <- cnetplot(x, showCategory=10, colorEdge=TRUE, categorySize="pvalue", layout="nicely")
=======
p <- cnetplot(x, showCategory=10, categorySize="pvalue", layout="nicely")
>>>>>>> 954b8a0e89a7b5cd6bc90a93d630617bf8c91822
#return(list("iD3"=iD3))# "p_cnet" = p_cnet))
p
})
outputOptions(output, "conditionDataEntry", suspendWhenHidden = FALSE)
outputOptions(output, "conditionNameEntry", suspendWhenHidden = FALSE)
outputOptions(output, "conditionListEntry", suspendWhenHidden = FALSE)
outputOptions(output, "checkNo", suspendWhenHidden = FALSE)
####LRT Tables####
# function to run mcdavid et al. DE test
DifferentialLRT <- function(x, y, xmin = 0) {
lrtX <- bimodLikData(x = x)
lrtY <- bimodLikData(x = y)
lrtZ <- bimodLikData(x = c(x, y))
lrt_diff <- 2 * (lrtX + lrtY - lrtZ)
return(pchisq(q = lrt_diff, df = 3, lower.tail = F))
}
MinMax <- function(data, min, max) {
data2 <- data
data2[data2 > max] <- max
data2[data2 < min] <- min
return(data2)
}
bimodLikData <- function(x, xmin = 0) {
x1 <- x[x <= xmin]
x2 <- x[x > xmin]
xal <- MinMax(
data = length(x = x2) / length(x = x),
min = 1e-5,
max = (1 - 1e-5)
)
likA <- length(x = x1) * log(x = 1 - xal)
if (length(x = x2) < 2) {
mysd <- 1
} else {
mysd <- sd(x = x2)
}
likB <- length(x = x2) *
log(x = xal) +
sum(dnorm(x = x2, mean = mean(x = x2), sd = mysd, log = TRUE))
return(likA + likB)
}
LRTTableFull <- reactive({
n <- nrow(dataClusterNames())
sampleInd <- combn(1:n,2)
sampleNames <- combn(t(dataClusterNames()),2)
lrTable <- matrix(NA,n,n)
dat_full <- dataGeneExpressionCluster()
for (i in seq_len(ncol(sampleInd))){
m = length(dat_full[dat_full$Cluster==sampleNames[1,i],]$Gene)
k = length(dat_full[dat_full$Cluster==sampleNames[2,i],]$Gene)
if (m<3 | k<3){
lrTable[sampleInd[1,i],sampleInd[2,i]] <- NA
lrTable[sampleInd[2,i],sampleInd[1,i]] <- NA
} else {
x_vec <- dat_full[dat_full$Cluster==sampleNames[1,i],]$Gene
y_vec <- dat_full[dat_full$Cluster==sampleNames[2,i],]$Gene
if (input$lrTypeFull == "P"){
sampleLR <- DifferentialLRT(x_vec,y_vec)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- sampleLR
lrTable[sampleInd[2,i],sampleInd[1,i]] <- sampleLR
} else if (input$lrTypeFull == "F"){
FC <- round(mean(x_vec)/mean(y_vec),digits=3)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- FC
lrTable[sampleInd[2,i],sampleInd[1,i]] <- 1/FC
} else if (input$lrTypeFull == "L") {
FC <- round(mean(x_vec)/mean(y_vec),digits=3)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- log2(FC)
lrTable[sampleInd[2,i],sampleInd[1,i]] <- log2(1/FC)
}
}
}
colnames(lrTable) <- c(t(dataClusterNames()))
rownames(lrTable) <- t(dataClusterNames())
return(lrTable)
})
output$LRTTableFull <- renderTable({
LRTTableFull()
},spacing = "m",align = 'r',rownames = TRUE, digits = -1, na = "",bordered = TRUE) #, digits = -1
LRTTablePos <- reactive({
it <- thresholdCheck(input$threshold)
n <- nrow(dataClusterNames())
sampleInd <- combn(1:n,2)
sampleNames <- combn(t(dataClusterNames()),2)
lrTable <- matrix(NA,n,n)
dat_pos <- dataGeneExpressionCluster()[dataGeneExpressionCluster()$Gene>it,]
for (i in seq_len(ncol(sampleInd))){
m = length(dat_pos[dat_pos$Cluster==sampleNames[1,i],]$Gene)
k = length(dat_pos[dat_pos$Cluster==sampleNames[2,i],]$Gene)
if (m<3 | k<3){
lrTable[sampleInd[1,i],sampleInd[2,i]] <- NA
lrTable[sampleInd[2,i],sampleInd[1,i]] <- NA
} else {
x_vec <- dat_pos[dat_pos$Cluster==sampleNames[1,i],]$Gene
y_vec <- dat_pos[dat_pos$Cluster==sampleNames[2,i],]$Gene
if (input$lrTypePositive == "P"){
sampleLR <- DifferentialLRT(x_vec,y_vec)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- sampleLR
lrTable[sampleInd[2,i],sampleInd[1,i]] <- sampleLR
} else if (input$lrTypePositive == "F"){
FC <- round(mean(x_vec)/mean(y_vec),digits=3)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- FC
lrTable[sampleInd[2,i],sampleInd[1,i]] <- 1/FC
} else if (input$lrTypePositive == "L"){
FC <- round(mean(x_vec)/mean(y_vec),digits=3)
lrTable[sampleInd[1,i],sampleInd[2,i]] <- log2(FC)
lrTable[sampleInd[2,i],sampleInd[1,i]] <- log2(1/FC)
}
}
}
colnames(lrTable) <- c(t(dataClusterNames()))
rownames(lrTable) <- t(dataClusterNames())
return(lrTable)
})
output$LRTTablePos <- renderTable({
LRTTablePos()
}, spacing = "m",align = 'r',rownames = TRUE, digits = -1, na = "",bordered = TRUE) #,spacing = "m",align = 'r'
###QUAN QC PLots####
libSizeExprsGenesFull <- reactive({
expression.matrix <- as.data.frame(dataExpression())
cell.information <- dataCluster()[,1]
batch.names <- unique(cell.information)
## Subset the data
total.counts <- log1p(colSums(expression.matrix))
total.features <- apply(expression.matrix, 2, function(x){sum(x>0)})
## Tidy dataframe
combined.data <- list(TotalCounts = total.counts, FeatureCounts = total.features, BatchInfo = cell.information)
combined.df <- as.data.frame(combined.data)
# ggplot
gradient.ramp <- ggplot2::scale_colour_gradient(low="#001b7f", high="#f1d351")
control.plot <- ggplot2::ggplot(combined.df, ggplot2::aes(x = factor(BatchInfo), y = TotalCounts, colour = FeatureCounts))
# control.plot <- control.plot + ggplot2::geom_violin(size = 1, scale = "width", colour = NA)
control.plot <- control.plot + ggbeeswarm::geom_quasirandom(shape = 16, size=5, alpha=0.5, dodge.width=0.5, groupOnX = T)
control.plot <- control.plot + gradient.ramp + ggplot2::scale_x_discrete(limits = batch.names) + ggplot2::xlab("Sample") + ggplot2::ylab("Total mapped reads per cell (log)")
control.plot <- control.plot + ggplot2::ggtitle("Total mapped reads and genes per sample")
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(control.plot) %>% layout(margin = m)
p
})
output$QClibSizeExprsGenesFull <- renderPlotly(libSizeExprsGenesFull())
GeneFull <- reactive({
expression.matrix <- as.data.frame(dataExpression())
expression.gene <- dataGeneExpressionCluster()$Gene
cell.information <- dataGeneExpressionCluster()$Cluster
batch.names <- unique(cell.information)
## Subset the data
total.counts <- colSums(expression.matrix)
gene.percent <- expression.gene/total.counts*100
## Tidy dataframe
combined.data <- list(genePercent = gene.percent , ExprsLevel = expression.gene, BatchInfo = cell.information)
combined.df <- as.data.frame(combined.data)
# ggplot
gradient.ramp <- ggplot2::scale_colour_gradient(low="#001b7f", high="#f1d351")
Gene.plot <- ggplot2::ggplot(combined.df, ggplot2::aes(x = factor(BatchInfo), y = genePercent, colour = ExprsLevel))
Gene.plot <- Gene.plot + ggplot2::geom_violin(size = 1, scale = "width", colour = NA) +
ggbeeswarm::geom_quasirandom(shape = 16, size=5, alpha=0.5, dodge.width=0.5, groupOnX = T)
Gene.plot <- Gene.plot + gradient.ramp + ggplot2::scale_x_discrete(limits = batch.names) +
ggplot2::xlab("Sample") + ggplot2::ylab("Percent")
Gene.plot <- Gene.plot + ggplot2::ggtitle(paste0("Percent Expressed of the Selected Gene: ", input$name))
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(Gene.plot) %>% layout(margin = m)
p
})
output$QCGeneFull <- renderPlotly(GeneFull())
####QC Plots Drop out####
DropOutFull <- reactive({
expression.matrix <- as.data.frame(dataExpression())
## Subset the data
totalCells <- ncol(expression.matrix)
DropOut <- apply(expression.matrix, 1, function(x){sum(x>0)})/totalCells
MeanExprs <- rowMeans(expression.matrix)
MeanExprs <-log2(MeanExprs+1)
## Tidy dataframe
combined.data <- list(PropExprs = DropOut , MeanExprs = MeanExprs)
combined.df <- as.data.frame(combined.data)
combined.df <- combined.df[order(combined.df$MeanExprs, decreasing=TRUE ),]
# ggplot
DropOut.plot <- ggplot2::ggplot(combined.df, ggplot2::aes(x = MeanExprs, y = PropExprs))
DropOut.plot <- DropOut.plot + ggplot2::geom_point() + guides(colour=FALSE)
DropOut.plot <- DropOut.plot + ggplot2::ggtitle("Proportion of Cells Expressed by Mean Expression") +xlab("Log2 mean expression") +
ylab("Proportion of Cells Expressed")
m <- list(l = 80, b = 40, t = 95)
p <- ggplotly(DropOut.plot) %>% layout(margin = m)
p
})
output$DropOutFull <- renderPlotly(DropOutFull())
#removing outliers (QC tab)
outlierRemoval <- reactive({
expression.matrix <- as.data.frame(dataExpression())
## Subset the data
totalCells <- ncol(expression.matrix)
#Checking cells
LibSizes <- colSums(expression.matrix)
LibSizes <- as.numeric(as.vector(LibSizes))
nDetectedGenes <- apply(expression.matrix, 2, function(x){sum(x>0)})
nDetectedGenes <- as.numeric(as.vector(nDetectedGenes))
##threshold from slider
mad_cutoff <- input$mad_cutoff
lowerMAD1 <- median(nDetectedGenes) - mad_cutoff*mad(nDetectedGenes)
higherMAD1 <- median(nDetectedGenes) + mad_cutoff*mad(nDetectedGenes)
Cells_removed_MADgenes <- which((nDetectedGenes < lowerMAD1) | (nDetectedGenes > higherMAD1))
lowerMAD2 <- median(LibSizes) - mad_cutoff*mad(LibSizes)
higherMAD2 <- median(LibSizes) + mad_cutoff*mad(LibSizes)
Cells_removed_MAD_libsizes <- which((LibSizes < lowerMAD2) | (LibSizes > higherMAD2))
#Checking genes
pcExprCells <- apply(expression.matrix, 1, function(x){sum(x>0)})/totalCells*100
pcExprCells <- as.numeric(as.vector(pcExprCells))
lowerMAD3 <- median(pcExprCells) - mad_cutoff*mad(pcExprCells)
higherMAD3 <- median(pcExprCells) + mad_cutoff*mad(pcExprCells)
Genes_removed_MAD_pcExprs <- which((pcExprCells < lowerMAD3) | (pcExprCells > higherMAD3))
##percent expression threshold from slider
gene_lower_than_threshold <- which(pcExprCells < input$gene_cutoff)
#final data to keep
cells_to_remove <- unique(c(Cells_removed_MADgenes,Cells_removed_MAD_libsizes))
genes_to_remove <- gene_lower_than_threshold
data_kept <- expression.matrix[-genes_to_remove, -cells_to_remove]
cluster_all <-dataCluster()
cluster_kept <- cluster_all[-cells_to_remove]
# output dataframe
combined.data <- list(OutlierCellsLibSizes = length(Cells_removed_MAD_libsizes),
OutlierCellsDetectedGenes = length(Cells_removed_MADgenes),
OutlierGenesMADExprs = length(Genes_removed_MAD_pcExprs),
OutlierGenesPercentExprs = length(gene_lower_than_threshold))
combined.df <- as.data.frame(combined.data)
filtered.df <- data_kept
list_output <-list("summary"=combined.df, "data" = filtered.df, "cluster"= cluster_kept)
})
output$outlierRemoved <- renderTable({
as.matrix(outlierRemoval()$summary)
},bordered = TRUE)
#Normalisation (QC tab)
normalisation <- reactive({
expression.matrix <- as.data.frame(outlierRemoval()$data)
if(input$normOption == "CPM"){
dat_norm <-apply(expression.matrix, 2, function(x){(x/sum(x))*1000000})
}else if (input$normOption == "Quantile"){
#the function quantile_normalisation is based on DaveTang code
quantile_normalisation <- function(df){
df_rank <- apply(df,2,rank,ties.method="min")
df_sorted <- data.frame(apply(df, 2, sort))
df_mean <- apply(df_sorted, 1, mean)
index_to_mean <- function(ip_index, ip_mean){
return(ip_mean[ip_index])
}
df_final <- apply(df_rank, 2, index_to_mean, ip_mean=df_mean)
rownames(df_final) <- rownames(df)
return(df_final)
}
dat_norm <-quantile_normalisation(as.matrix(expression.matrix))
dat_norm <-as.data.frame(dat_norm)
}else if(input$normOption == "RLE"){
#Need to cite RLE method in the ascend package
CalcNormFactor <- function(x, geo.means) {
x.geo.means <- cbind(x, geo.means)
x.geo.means <- x.geo.means[(x.geo.means[, 1] > 0), ]
non.zero.median <- median(apply(x.geo.means, 1, function(y) {
y <- as.vector(y)
y[1]/y[2]
}))
return(non.zero.median)
}
CalcGeoMeans <- function(x) {
x <- x[x > 0]
x <- exp(mean(log(x)))
return(x)
}
geo.means <- apply(expression.matrix, 1, CalcGeoMeans)
norm.factor <- apply(expression.matrix, 2, function(x) CalcNormFactor(x, geo.means))
dat_norm <- t(t(expression.matrix)/norm.factor)
}else{
dat_norm <- expression.matrix
}
})
output$downloadNormData = downloadHandler(
#s = input$x1_rows_all
filename = function() {
paste("norm_output_data_", Sys.time(),".csv", sep="")
},
content = function(file) {
write.csv(normalisation(), file)
}
)
output$downloadClusterData = downloadHandler(
#s = input$x1_rows_all
filename = function() {