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server.R
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server.R
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server <- function(input, output) {
plotInput <- function(){
inFile <- input$file
file1 <- read.csv(inFile$datapath, header = T)
# file1 <- read.csv("/Users/srnallan/Desktop/Heatmap Shiny/test shiny.csv", header = T)
mainfile <- as.data.frame(file1[4:nrow(file1), c(1,4:ncol(file1))])
rownames(mainfile) <- mainfile[,1]
mainfile <- mainfile[,-1]
for( i in 1:ncol(mainfile)){
mainfile[,i] <- as.character(mainfile[,i])
mainfile[,i] <- as.integer(mainfile[,i])
}
mainfile <- as.matrix(mainfile)
colanno <- data.frame(t(file1[2:3, 4:ncol(file1)]))
colnames(colanno)[[1]] <- as.character(file1[2,3])
colnames(colanno)[[2]] <- as.character(file1[3,3])
file2 <- data.frame(t(file1[1,4:ncol(file1)]))
file2$Sample <- rownames(file2)
file2 <- file2[,c(2,1)]
colnames(file2) <- c("Sample", "Total.reads")
file2$Lib.size <- colSums(mainfile)
file2$Total.reads <- as.character(file2$Total.reads)
file2$Total.reads <- as.integer(file2$Total.reads)
file2$Percentage.reads.mapped <- file2$Lib.size/file2$Total.reads
file3 <- file1[4:nrow(file1), 1:3]
rownames(file3) <- file3[,1]
# file1 <- file1[-(1:3),-(2:3)]
# rownames(file1) <- file1[,1]
# file1 <- file1[,-1]
if(all(colnames(mainfile) != rownames(file2))) return("Please check the sample names")
if(all(rownames(mainfile) != rownames(file3))) return("Please check the sample names")
# Create the expression set
pd <- new("AnnotatedDataFrame", data = file2, varMetadata = data.frame(cbind(colnames(file2), rep("stuff", length(colnames(file2))))))
fd <- new("AnnotatedDataFrame", data = file3, varMetadata = data.frame(cbind(colnames(file3), rep("stuff", length(colnames(file3))))))
hdat.eset <- new("ExpressionSet", phenoData = pd, exprs = as.matrix(mainfile),featureData = fd)
Counts <- exprs(hdat.eset)
dim(Counts)
tmr <- 500000
e2e <- 0.6
hdat.eset <- hdat.eset[,hdat.eset$Lib.size > tmr & hdat.eset$Percentage.reads.mapped > e2e]
hdat.eset #9 samples 137 genes
#### Create DGEList
y <- DGEList(count = exprs(hdat.eset), genes= file3, remove.zeros=T, lib.size = colSums(exprs(hdat.eset)))
# tapply(y$samples$lib.siz, INDEX= y$samples$group, summary)
# vv <- y$samples[,1:2]
# y$counts <- y$counts+10
######## Filtering Removing absolute zero counts
# y <- y[!y[[3]][,3]=="Fusion",]
selr <- rowSums(cpm(y)>1) >= ncol(y) #Filter low expression tags: keep genes with at least 5 CPM in at least X, where X is the number of samples in the smallest group
selc <- colSums(y$counts)>=500000 #Filter samples based on library size
y <- y[selr,selc, keep.lib.sizes=F]
dim(y)
# Traditional EdgeR library normalization
y$samples$lib.size <- colSums(y$counts)
y.reg <- calcNormFactors(y, method = c("TMM"))
# Custom Normalization for targeted gene panel
housekeeping <- as.character(y[[3]][y[[3]][,3]=="Housekeeping",1])
counts <- as.matrix(y$counts)
counts <- t(counts)
NormalizeMxRNASeq <- function(counts,housekeeping) {
stopifnot(class(counts)=="matrix")
stopifnot(class(housekeeping)=="character")
stopifnot(housekeeping %in% colnames(counts))
log2Counts <- log2(counts+1)
log2Housekeeping <- log2Counts[,housekeeping]
housekeepingMeans <- apply(log2Housekeeping,1,mean)
log2Normalized <- log2Counts-housekeepingMeans
log2Normalized <- t(log2Normalized)
return(log2Normalized)
}
y.new <- NormalizeMxRNASeq(counts, housekeeping)
y.new.med <- rowMedians(y.new)
y.new.1 <- y.new-y.new.med
HKnorm <- data.frame(y.new.1)
HKnorm$Comparison <- "Housekeeping normalization"
################### Heatmap norm no DE Trad edgeR CPM
e <- cpm(y.reg, normalized.lib.sizes = T, prior.count = 0.25, log=TRUE)
med.e <- rowMedians(e)
e.med <- e-med.e
edgernorm <- data.frame(e.med)
edgernorm$Comparison <- "edgeR TMM normalization"
df <- data.frame(rbind(HKnorm, edgernorm))
# df.x <- as.matrix(droplevels(df[df$Comparison %in% c("Housekeeping normalization"), c(1:(ncol(df)-1))]))
df.x <- as.matrix(droplevels(df[df$Comparison %in% input$select_class, c(1:(ncol(df)-1))]))
rownames(df.x) <- rownames(y$genes)
rowanno <- data.frame(y[[3]][,c(2,3)])
rowanno.1 <- data.frame(rowanno[,2])
colnames(rowanno.1)[[1]] <- "Function"
rownames(rowanno.1) <- rownames(rowanno)
colanno <- colanno[rownames(colanno) %in% rownames(y[[2]]),]
if(all(rownames(y[[2]]) != rownames(colanno))) return("Please check the sample row names")
colanno[,1] <- factor(colanno[,1])
colanno[,2] <- factor(colanno[,2])
colors.hm <- colorRampPalette(c("blue","white","red"))(100)
heat.brks <- seq(from=-max(abs(df.x)), to=max(abs(df.x)),length= 101)
# pheatmap(e.med ,color=colors.hm, labels_row = y$genes$Gene,fontsize_row = 5,
# # cluster_cols = ifelse(input$select_class_1=="Yes", T, F),
# annotation_row = rowanno.1)
pheatmap(df.x ,color=colors.hm, breaks=heat.brks, labels_row = y[[3]][,2], main=paste0(input$select_class),fontsize_row = 10,
cluster_cols = ifelse(input$select_class_1=="Yes", T, F), annotation_row = rowanno.1, annotation_col = colanno,
clustering_method = input$select_class_2)
}
output$distPlot <- renderPlot({
# input$file1 will be NULL initially. After the user selects
# and uploads a file, it will be a data frame with 'name',
# 'size', 'type', and 'datapath' columns. The 'datapath'
# column will contain the local filenames where the data can
# be found.
inFile <- input$file
if (is.null(inFile))
return(NULL)
# read.csv(inFile$datapath, header = input$header)
plotInput()
},height= 950)
output$export <- downloadHandler(
filename = function() { paste(gsub(".csv","",input$file), " Clustering- ", input$select_class_2,' normalized heatmap.pdf', sep='') },
content = function(file) {
ggsave(file, plot = plotInput(), device = "pdf", width=20,height = 20)
}
)
}
# Run the application
shinyApp(ui = ui, server = server)