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server-inputdata.R
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## ==================================================================================== ##
# START Shiny App for analysis and visualization of transcriptome data.
# Copyright (C) 2016 Jessica Minnier
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# You may contact the author of this code, Jessica Minnier, at <[email protected]>
## ==================================================================================== ##
observe({
# Check if example selected, or if not then ask to upload a file.
validate(
need((input$data_file_type=="examplecounts")|((!is.null(input$rdatafile))|(!is.null(input$datafile))),
message = "Please select a file")
)
inFile <- input$datafile
if(!is.null(inFile)) {
# update options for various analyzed data columns
if(input$inputdat_type=="analyzed") {
print("updating analyzed data choices")
seqdata <- inputDataReactive()$data
tmpcols = colnames(seqdata)
updateSelectInput(session,"c_geneid1",choices =tmpcols)
updateSelectInput(session,"c_geneid2",choices =tmpcols)
updateSelectInput(session,"c_expr1",choices =tmpcols)
updateSelectInput(session,"c_expr2",choices =tmpcols)
updateSelectInput(session,"c_fc1",choices =tmpcols)
updateSelectInput(session,"c_fc2",choices =tmpcols)
updateSelectInput(session,"c_pval1",choices =tmpcols)
updateSelectInput(session,"c_pval2",choices =tmpcols)
}
}
})
inputDataReactive <- reactive({
# input$file1 will be NULL initially. After the user selects
# and uploads a file, it will be a data frame with 'name',
# 'size', 'type', and 'datapath' columns. The 'datapath'
# column will contain the local filenames where the data can
# be found.
print("inputting data")
# Check if example selected, or if not then ask to upload a file.
validate(
need((input$data_file_type=="examplecounts")|((!is.null(input$rdatafile))|(!is.null(input$datafile))),
message = "Please select a file")
)
inFile <- input$datafile
inRFile <- input$rdatafile
# browser()
if(input$data_file_type=="examplecounts") {
# upload example data
seqdata <- read_csv("data/mousecounts_example.csv")
print("uploaded mousecounts data")
return(list('data'=seqdata))
}else if(input$data_file_type=="previousrdata"){
if (!is.null(inRFile)) {
load(inRFile$datapath,envir=environment())
return(list("data"=data_results_table)) # this is so something shows in data upload window
}else{return(NULL)}
}else { # if uploading data
if (!is.null(inFile)) {
seqdata <- read_csv(inFile$datapath)
print('uploaded seqdata')
if(ncol(seqdata)==1) { # if file appears not to work as csv try tsv
seqdata <- read_tsv(inFile$datapath)
print('changed to tsv, uploaded seqdata')
}
validate(need(ncol(seqdata)>1,
message="File appears to be one column. Check that it is a comma-separated (.csv) file."))
return(list('data'=seqdata))}else{return(NULL)}
}
})
# check if a file has been uploaded and create output variable to report this
output$fileUploaded <- reactive({
return(!is.null(inputDataReactive()))
})
outputOptions(output, 'fileUploaded', suspendWhenHidden=FALSE)
# after the data is uploaded or example data is selected, analyze the data
analyzeDataReactive <-
eventReactive(input$upload_data,
ignoreNULL = FALSE, {
withProgress(message = "Analyzing RNA-seq data, please wait",{
print("analysisCountDataReactive")
#if an example just return previously analyzed results
if(input$data_file_type=="examplecounts") {
load('data/mousecounts_example_analysis_results.RData')
load('data/mousecounts_example_analyzed.RData') #example_data_results
return(list('group_names'=group_names,'sampledata'=sampledata,
"results"=results,"data_long"=data_long, "geneids"=geneids,
"expr_data"=expr_data,"data_results_table"=example_data_results))
}
#if uploading own data:
if(input$data_file_type=="previousrdata"){
inRfile <- input$rdatafile
load(inRfile$datapath,envir=environment())
return(list('group_names'=group_names,'sampledata'=sampledata,
"results"=results,"data_long"=data_long,
"geneids"=geneids, "expr_data"=expr_data,
"data_results_table"=data_results_table))
}
alldata <- inputDataReactive()$data
# Check for numeric columns
not_numeric <- function(input) {
if(sum(unlist(lapply(input,function(k) class(k)%in%c("numeric","integer"))))==0) {
"Your data does not appear to be formatted correctly (no numeric columns).
Please check your input file."
} else if (input == "") {
FALSE
} else {
NULL
}
}
validate(
not_numeric(alldata)
)
# remove empty columns
alldata = alldata[,colMeans(is.na(alldata))<1]
if(input$inputdat_type=="counts") {
numgeneids <- 0
#catch incorrect gene id error, only works if geneids are 1:numgeneids and no other columns are characters
numgeneids = max(numgeneids,max(which(sapply(alldata,class)=="character")))
validate(need(numgeneids>0,
message = "You have no columns with characters, check that you have at least one column of gene ids in your file.")
)
tmpgenecols = 1:numgeneids
tmpexprcols = setdiff(1:ncol(alldata),tmpgenecols)
validate(need(length(tmpexprcols)>0,
message = "Your last column has characters. Check that your count data is numeric and
that your gene ids are in the first (left) columns only."))
tmpfccols = NULL
tmppvalcols = NULL
}
if(input$inputdat_type=="analyzed") {
tmpgenecols = seq(match(input$c_geneid1,colnames(alldata)),match(input$c_geneid2,colnames(alldata)))
tmpexprcols = seq(match(input$c_expr1,colnames(alldata)),match(input$c_expr2,colnames(alldata)))
tmpfccols = seq(match(input$c_fc1,colnames(alldata)),match(input$c_fc2,colnames(alldata)))
tmppvalcols = seq(match(input$c_pval1,colnames(alldata)),match(input$c_pval2,colnames(alldata)))
validate(need(length(tmpfccols)==length(tmppvalcols),message =
"Number of fold change columns needs to be same number as p-value columns (and in the same order)."))
}
#split expression names into groups
sampleid <- colnames(alldata[,tmpexprcols])
tmpnames <- do.call(rbind,strsplit(sampleid,"_",fixed=TRUE))
group_names <- unique(tmpnames[,1])
group <- tmpnames[,1]
rep_id <- tmpnames[,2]
sampledata = data.frame(sampleid,group,rep_id)
countdata <- alldata[,tmpexprcols,drop=FALSE]
geneids <- alldata[,tmpgenecols,drop=FALSE]
tmpkeep = which(apply(is.na(geneids),1,mean)<1) #remove rows with no gene identifiers
print(paste0("Num genes kept after removing empty geneids: ",
length(tmpkeep)," of ", nrow(geneids)))
validate(need(length(tmpkeep)>0,message = "Your data is empty. Please check file format is .csv. You may need a non-empty gene identifier column."))
geneids = geneids[tmpkeep,,drop=FALSE]
countdata = countdata[tmpkeep,,drop=FALSE]
# Create unique identifier
geneids = geneids%>%unite_("unique_id",colnames(geneids),remove = FALSE)
#if geneids not unique
if(length(unique(geneids$unique_id))<nrow(geneids)) {
geneids = geneids%>%group_by(unique_id)%>%
mutate(rn=row_number(unique_id),new=
ifelse(rn==1,unique_id,paste(unique_id,rn,sep="_")))%>%
ungroup()%>%mutate(unique_id=new)%>%select(-rn,-new)
}
#add filter for max # counts
#handle NAs, update this later
countdata[which(is.na(countdata),arr.ind=T)] <- 0 #allow choice of this or removal
rownames(countdata) = geneids$unique_id
if(input$inputdat_type=="analyzed") {
expr_data <- alldata[tmpkeep,tmpexprcols,drop=FALSE]
rownames(expr_data) = geneids$unique_id
tmpfc = alldata[tmpkeep,tmpfccols,drop=F]
if(input$isfclogged=="No (Log my data please)") {log2(tmpfc)}
fcdata = cbind("unique_id"=geneids$unique_id,tmpfc)
pvaldata = cbind("unique_id"=geneids$unique_id,alldata[tmpkeep,tmppvalcols,drop=F])
tmpnames = paste(colnames(fcdata),colnames(pvaldata),sep=":")[-1]
colnames(fcdata)[-1] = tmpnames
colnames(pvaldata)[-1] = tmpnames
fcdatalong = fcdata%>%gather(key = "test",value = "logFC",-1)
pvaldatalong = pvaldata%>%gather(key = "test",value = "P.Value",-1)
tmpres = full_join(fcdatalong,pvaldatalong)
tmpdat = cbind("unique_id"=geneids$unique_id,expr_data)
tmpdatlong = tmpdat%>%gather(key="sampleid",value="expr",-1)
data_long = left_join(tmpdatlong,sampledata%>%select(sampleid,group))
tmpres$test = as.character(tmpres$test)
return(list('group_names'=group_names,'sampledata'=sampledata,
"results"=tmpres,"data_long"=data_long,
"geneids"=geneids,"expr_data"=expr_data,
"data_results_table"=alldata))
}else if(input$inputdat_type=="counts") {
#analyze data
# not_counts <- function(input) {
# remainder = sum(apply(input,2,function(k) sum(k%%1,na.rm=T)),na.rm=T)
# if (remainder !=0) {
# "Your data appears to not be counts, please double check your data"
# } else if (input == "") {
# FALSE
# } else {
# NULL
# }
# }
# Check if data appears to be integer counts. If not, skip voom.
datacounts <- function(input) {
remainder = sum(apply(input,2,function(k) sum(k%%1,na.rm=T)),na.rm=T)
if (remainder ==0) {
TRUE
} else {
FALSE
}
}
#do not perform voom on non-counts and assumpe log2 uploaded intensities
dovoom= datacounts(countdata)
# if(not_counts(countdata)){print("Warning: You are uploading data that does not appear to be counts, the analysis pipeline will not be valid!")}
# validate(
# not_counts(countdata)
# )
print("analyze data: counts")
# Only one group
if(nlevels(sampledata$group)<2) {
design <- matrix(1,nrow=nrow(sampledata),ncol=1)
colnames(design) = "(Intercept)"
}else{
design <- model.matrix(~0+sampledata$group) # allow selection of reference group
colnames(design) = levels(as.factor(sampledata$group))
}
if(dovoom) {
#voom+limma
dge <- DGEList(counts=countdata) #TMM normalization first
dge <- calcNormFactors(dge)
log2cpm <- cpm(dge, prior.count=0.5, log=TRUE)
if(max(colSums(design)==1)) {
# if only one replicate for each group
v <- voom(dge,normalize.method = "cyclicloess")
}else{
v <- voom(dge,design,plot=FALSE,normalize.method = "cyclicloess")
}
# v <- voom(countdata,design,plot=TRUE,normalize="quantile") #use this to allow different normalization
#fit <- lmFit(v,design)
#fit <- eBayes(fit)
expr_data = v$E
}else{
print("not doing voom")
countdata2 = countdata
# crude check for logged data, unlikely to have a logged value >1000
if(max(countdata)>1000) countdata2 = log2(countdata+0.5)
log2cpm = countdata2
expr_data = countdata2
}
tmpgroup = sampledata$group
#contrasts(tmpgroup)
if(length(group_names)==1) { #If only one group no tests
lmobj_res = data.frame(matrix(NA,nrow=nrow(expr_data),ncol=6))
colnames(lmobj_res) = c("test","dneom_group","numer_group","logFC","P.Value","adj.P.Val")
lmobj_res = cbind("unique_id"=geneids$unique_id,lmobj_res)
lmobj_res$numer_group = group_names[1]
lmobj_res$test = "None"
}else{
lmobj_res = list()
for(ii in 1:length(group_names)) {
grp = relevel(tmpgroup,ref= group_names[ii] )
lm.obj = lm(t(expr_data) ~ grp)
beta.lm = t(lm.obj$coefficients)
pval.lm = t(lm.pval(lm.obj)$pval)
pval.adj.lm = apply(pval.lm,2,p.adjust,method="BH")
colnames(beta.lm) = colnames(pval.lm) = colnames(pval.adj.lm) =
gsub("grp","",colnames(beta.lm))
tmpout = cbind(melt(beta.lm[,-1,drop=FALSE]),
melt(pval.lm[,-1,drop=FALSE])$value,
melt(pval.adj.lm[,-1,drop=FALSE])$value)
colnames(tmpout) = c("unique_id","numer_group","logFC","P.Value","adj.P.Val")
tmpout$denom_group = group_names[ii]
tmpout$test = with(tmpout, paste(numer_group,denom_group,sep="/"))
tmpout = tmpout[,c("unique_id","test","denom_group","numer_group",
"logFC","P.Value","adj.P.Val")]
lmobj_res[[ii]] = tmpout
}
lmobj_res = do.call(rbind,lmobj_res)
}
# matrix of pvalues with each column a type of test, same for logfc
pvals = lmobj_res%>%select(unique_id,test,adj.P.Val)%>%spread(test,adj.P.Val)
logfcs = lmobj_res%>%select(unique_id,test,logFC)%>%spread(test,logFC)
colnames(pvals)[-1] = paste0("padj_",colnames(pvals)[-1])
colnames(logfcs)[-1] = paste0("logFC_",colnames(logfcs)[-1])
tmpdat = cbind(geneids,log2cpm)
tmpdat = left_join(tmpdat,logfcs)
tmpdat = left_join(tmpdat,pvals)
data_results_table = tmpdat%>%select(-unique_id) #save this into csv
tmpexprdata = data.frame("unique_id" =geneids$unique_id,expr_data)
tmpcountdata = data.frame("unique_id"=geneids$unique_id,countdata)
tmplog2cpm = data.frame("unique_id"=geneids$unique_id,log2cpm)
log2cpm_long = melt(tmplog2cpm,variable.name = "sampleid",value.name="log2cpm")
countdata_long = melt(tmpcountdata,variable.name = "sampleid",value.name="count")
#countdata_long$log2count = log2(countdata_long$count+.25)
exprdata_long = melt(tmpexprdata,variable.name = "sampleid",value.name="log2cpm_voom")
data_long = left_join(countdata_long,log2cpm_long)
data_long = left_join(data_long,exprdata_long)
data_long$group = do.call(rbind,strsplit(as.character(data_long$sampleid),"_",fixed=TRUE))[,1]
tmpgeneidnames = colnames(geneids%>%select(-unique_id))
if(length(tmpgeneidnames)>0) {
data_long = data_long%>%select(-one_of(tmpgeneidnames))
}
#expr_data = tmplog2cpm[,-1]
print('analyze data: done')
return(list('group_names'=group_names,'sampledata'=sampledata,
"results"=lmobj_res,"data_long"=data_long, "geneids"=geneids,
"expr_data"=expr_data,
"data_results_table"=data_results_table))
}
})
})
output$countdataDT <- renderDataTable({
tmp <- inputDataReactive()
if(!is.null(tmp)) tmp$data
})
observeEvent(input$upload_data, ({
updateCollapse(session,id = "input_collapse_panel", open="analysis_panel",
style = list("analysis_panel" = "success",
"data_panel"="primary"))
}))
observeEvent(inputDataReactive(),({
updateCollapse(session,id = "input_collapse_panel", open="data_panel",
style = list("analysis_panel" = "default",
"data_panel"="success"))
})
)
output$analysisoutput <- DT::renderDataTable({
print("output$analysisoutput")
getresults <- analyzeDataReactive()
res = getresults$results
res[,sapply(res,is.numeric)] <- signif(res[,sapply(res,is.numeric)],3)
DT::datatable(res)
})
output$downloadResults_CSV <- downloadHandler(filename = paste0("START_results_",Sys.Date(),".csv"),
content = function(file) {
write.csv(analyzeDataReactive()$data_results_table, file, row.names=FALSE)})
output$downloadResults_RData <- downloadHandler(filename= paste0("START_results_",Sys.Date(),".RData"),
content=function(file){
tmp = analyzeDataReactive()
group_names = tmp$group_names
sampledata = tmp$sampledata
results = tmp$results
data_long = tmp$data_long
geneids = tmp$geneids
expr_data = tmp$expr_data
data_results_table = tmp$data_results_table
save(group_names,sampledata,results,
data_long,geneids,expr_data,
data_results_table,file=file)
})
output$example_counts_file <- downloadHandler(filename="examplecounts_short.csv",
content=function(file){
file.copy("data/examplecounts_short.csv",file)
})
output$example_analysis_file <- downloadHandler(filename="exampleanalysisres_short.csv",
content=function(file){
file.copy("data/exampleanalysisres_short.csv",file)
})
output$instructionspdf <- downloadHandler(filename="Instructions.pdf",
content=function(file){
file.copy("instructions/Instructions.pdf",file)
})