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server.R
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server.R
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library(shiny)
library(shinyBS)
library(shinysky)
library(shinyjs)
library(V8)
library(shinythemes)
library(plotly)
#webshot::install_phantomjs() #To plot into pdf Heatmaply
#Adding execution time limit
now <- Sys.time()
shinyServer(function(input, output,session){
#Adding execution time limit
observe({
invalidateLater(900000) #Test every 15 mins
print(paste("Actual Time: ", Sys.time(), " - Endtime: ", now + 10800))
if (Sys.time() > now + 10800) { #3 hours maximum time
print("Stop the App")
stopApp()
}
})
#Setting maximum file size for uploading (1000 MB)
options(shiny.maxRequestSize=1000*1024^2)
options(shiny.sanitize.errors = TRUE)
#Resolution of the tiff images
ppi<-200
#Hidding/Showing tabs of mainpanel, run and clear buttons, ...
observeEvent(input$run,{
shinyjs::hide(id="run")
shinyjs::show(id="after_run")
if (input$datatype=="TSV" & ((length(grep(".tsv",input[["fileinput"]]$datapath)) + length(grep(".txt",input[["fileinput"]]$datapath)))==length(input[["fileinput"]]$datapath))){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
mat <- fread(input[["fileinput"]]$datapath[w],header=T,sep="\t",data.table=F)
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector <- as.vector(do.call(cbind, mat_list))
} else {
mat_vector<-c("mat_vector")
}
if (input$datatype=="Excel" & (length(grep(".xls",input[["fileinput"]]$datapath))==length(input[["fileinput"]]$datapath))){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
if (length(grep(".xlsx",input[["fileinput"]]$datapath[w]))>0){
mat<-read.xlsx(input[["fileinput"]]$datapath[w],1)
} else {
mat<-data.frame(read_xls(input[["fileinput"]]$datapath[w],col_names = TRUE,col_types = "text"))
}
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector_2 <- as.vector(do.call(cbind, mat_list))
} else {
mat_vector_2<-c("mat_vector_2")
}
if (length(grep("FALSE", mat_vector))!=0 |
length(grep("FALSE", mat_vector_2))!=0 |
(input$datatype=="VCF" & length(grep(".vcf",input[["fileinput"]]$datapath))!=length(input[["fileinput"]]$datapath)) |
(input$datatype=="TSV" & ((length(grep(".tsv",input[["fileinput"]]$datapath)) + length(grep(".txt",input[["fileinput"]]$datapath)))!=length(input[["fileinput"]]$datapath))) |
(input$datatype=="Excel" & (length(grep(".xls",input[["fileinput"]]$datapath))!=length(input[["fileinput"]]$datapath))) |
(input$datatype=="MAF" & length(grep(".maf",input[["fileinput"]]$datapath))<=0) |
(input$datatype=="MAF" & length(input[["fileinput"]]$datapath)!=1)
){}else{
shinyjs::show(id="mainpanel")
}
})
observeEvent(input$clear, {
shinyjs::js$refresh()
})
#Library loading
library(MutationalPatterns)
library(reshape2)
library(ggplot2)
library(data.table)
library(VariantAnnotation)
library(heatmaply)
library(gplots)
library(openxlsx)
library(readxl)
#######################################
#Reference genome definition and loading [ref_genome]
#######################################
ref_genome<-eventReactive(input$run,{
if (input$genome=="19"){
library("BSgenome.Hsapiens.UCSC.hg19")
return ("BSgenome.Hsapiens.UCSC.hg19")
}
if (input$genome=="37"){
library ("BSgenome.Hsapiens.1000genomes.hs37d5")
return ("BSgenome.Hsapiens.1000genomes.hs37d5")
}
if (input$genome=="hg38"){
library("BSgenome.Hsapiens.UCSC.hg38")
return("BSgenome.Hsapiens.UCSC.hg38")
}
})
filedata <- reactive({
infile <- input$fileinput #file loaded
if (is.null(infile)) {
# User has not uploaded a file yet
return(NULL)
} else {return(infile)}
})
output$run_button <- renderUI({
if (!is.null(filedata())) {
div(
#Run button
actionButton("run","Run",class = "btn-primary"),
#Busy indicator
busyIndicator("Running",wait=0)
)
} else if (is.null(filedata())) {
HTML("")
}
})
#######WHEN RUN BUTTON##
observeEvent(input$run,{
all_ok_for_run <- TRUE
if (!is.null(filedata())) {
output$error_input <- renderUI({
HTML("All well!")
})
} else {
all_ok_for_run <- FALSE
}
#######################################
#Reading input files as GRanges objects [vcfs]
#######################################
vcfs<-eventReactive((all_ok_for_run==TRUE),{
inFile<<-input$fileinput
#VCF
if (input$datatype=="VCF"){
#Read vcf for MutationalPatterns
return(read_vcfs_as_granges(inFile$datapath,inFile$name,ref_genome(),group = "auto+sex"))#, check_alleles = TRUE))
}
#MAF
if (input$datatype=="MAF"){
aux<-fread(inFile$datapath,header=T,sep="\t",data.table=F)
aux<-aux[,c("Chromosome","Start_Position","Reference_Allele","Tumor_Seq_Allele2","Tumor_Sample_Barcode")]
colnames(aux)[1:4]<-c("#CHROM","POS","REF","ALT")
#Condition in case "chr" prefix is present at CHROM column in input file
if (length(grep("chr",aux))>0){
aux[,c("#CHROM")]<-sapply(strsplit(aux[,c("#CHROM")],"chr"),"[",2)
}
aux$ID<-"."
aux$QUAL<-"."
aux$FILTER<-"PASS"
aux<-aux[,c("#CHROM","POS","ID","REF","ALT","QUAL","FILTER","Tumor_Sample_Barcode")]
aux_list<-split(aux,f=aux$Tumor_Sample_Barcode)
aux_list<-lapply(aux_list,"[",c(1:7))
ff<-sapply(aux_list,function(i){tempfile(pattern="tp",fileext=".vcf")})
new_ff = do.call("c",lapply(sapply(ff,strsplit,"[.]"),paste,collapse="_new."))
for (i in 1:length(aux_list)){
write.table(aux_list[[i]],file=ff[i],row.names=F,quote=F,sep="\t")
system(paste0('echo "##fileformat=VCFv4.2" | cat - ',ff[i],' > ',new_ff[i]))
}
#validate(
# need(length(names(ff))<=20,"samplenumber"),errorClass="numberofsamples"
#)
return(read_vcfs_as_granges(new_ff,names(ff),ref_genome(),group = "auto+sex"))#, check_alleles = TRUE))
}
#TSV
if (input$datatype=="TSV"){
ff_list<-list()
new_ff_list = list()
for (w in 1:length(inFile$datapath)){
aux<-fread(inFile$datapath[w],header=T,sep="\t",data.table=F)
#Condition in case "chr" prefix is present at CHROM column in input file
if (length(grep("chr",aux))>0){
aux$CHROM<-sapply(strsplit(aux$CHROM,"chr"),"[",2)
}
colnames(aux)[1]<-"#CHROM"
aux$ID<-"."
aux$QUAL<-"."
aux$FILTER<-"PASS"
aux<-aux[,c("#CHROM","POS","ID","REF","ALT","QUAL","FILTER")]
ff_list[[w]]<-tempfile("tp",fileext=".vcf")
new_ff_list[[w]] = do.call("c",lapply(sapply(ff_list[[w]],strsplit,"[.]"),paste,collapse="_new."))
write.table(aux,file=ff_list[[w]],row.names=F,quote=F,sep="\t")
system(paste0('echo "##fileformat=VCFv4.2" | cat - ',ff_list[[w]],' > ',new_ff_list[[w]]))
}
ff<-do.call("c",new_ff_list)
return(read_vcfs_as_granges(ff,inFile$name,ref_genome(),group = "auto+sex"))#, check_alleles = TRUE))
}
#Excel
if (input$datatype=="Excel"){
ff_list<-list()
new_ff_list = list()
for (w in 1:length(inFile$datapath)){
if (length(grep(".xlsx",inFile$datapath[w]))>0){
aux<-read.xlsx(inFile$datapath[w],1)
} else {
aux<-data.frame(read_xls(inFile$datapath[w],col_names = TRUE,col_types = "text"))
}
#Condition in case "chr" prefix is present at CHROM column in input file
if (length(grep("chr",aux))>0){
aux$CHROM<-sapply(strsplit(aux$CHROM,"chr"),"[",2)
}
colnames(aux)[1]<-"#CHROM"
aux$ID<-"."
aux$QUAL<-"."
aux$FILTER<-"PASS"
aux<-aux[,c("#CHROM","POS","ID","REF","ALT","QUAL","FILTER")]
ff_list[[w]]<-tempfile("tp",fileext=".vcf")
new_ff_list[[w]] = do.call("c",lapply(sapply(ff_list[[w]],strsplit,"[.]"),paste,collapse="_new."))
write.table(aux,file=ff_list[[w]],row.names=F,quote=F,sep="\t")
system(paste0('echo "##fileformat=VCFv4.2" | cat - ',ff_list[[w]],' > ',new_ff_list[[w]]))
}
ff<-do.call("c",new_ff_list)
return(read_vcfs_as_granges(ff,inFile$name,ref_genome(),group = "auto+sex"))#, check_alleles = TRUE))
}
})
#######################################
#Mutation Matrix creation [mut_mat]
#######################################
mut_mat <- reactive({
if (input$genome=="19"){
return(mut_matrix(vcfs(),"BSgenome.Hsapiens.UCSC.hg19"))
}
if (input$genome=="37"){
return(mut_matrix(vcfs(),"BSgenome.Hsapiens.1000genomes.hs37d5"))
}
if (input$genome=="hg38"){
return(mut_matrix(vcfs(),"BSgenome.Hsapiens.UCSC.hg38"))
}
})
#######################################
#COSMIC Mutational Signatures loading (and adjustment) from COSMIC website [cancer_signatures]
#######################################
sp_url <- "http://cancer.sanger.ac.uk/cancergenome/assets/signatures_probabilities.txt"
cancer_signatures <- read.table(sp_url, sep = "\t", header = TRUE)
cancer_signatures_aux <- cancer_signatures[order(cancer_signatures[,1]),]
cancer_signatures <- as.matrix(cancer_signatures_aux[,4:33])
cancer_signatures_mut_types <- as.matrix(cancer_signatures_aux[,1:3])
#######################################
#Fitting mutations in samples (mut_mat) to COSMIC signatures [fit_res]
#######################################
fit_res <- reactive({ fit_to_signatures(mut_mat(), cancer_signatures) })
#Auxiliar files of aetiology and known signatures by cancer type (from COSMIC)
proposed_etiology <- fread("./aux_files/proposed_etiology_COSMIC_signatures.txt",sep="\t",header=F,data.table=F)[,2]
known_cancer_signatures<-read.table("./aux_files/cancermatrix.tsv",header=TRUE,sep="\t",row.names=1)
#divisionRel function creation to print final dataframe
divisionRel<-function(df){
sum_df<-sapply(df,sum)
for (i in 1:ncol(df)){
df[,i]<-round((df[,i]/sum_df[i]),3)
}
return(df)
}
################################################################
output$custom_error<-renderUI({
if (input$datatype=="TSV" & ((length(grep(".tsv",input[["fileinput"]]$datapath)) + length(grep(".txt",input[["fileinput"]]$datapath))) == length(input[["fileinput"]]$datapath))){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
mat <- fread(input[["fileinput"]]$datapath[w],header=T,sep="\t",data.table=F)
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector <- as.vector(do.call(cbind, mat_list))
samples_failed<-grep("FALSE", mat_vector)
} else {
mat_vector<-c("mat_vector")
if (input$datatype=="Excel" & (length(grep(".xls",input[["fileinput"]]$datapath))==length(input[["fileinput"]]$datapath))){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
if (length(grep(".xlsx",input[["fileinput"]]$datapath[w]))>0){
mat<-read.xlsx(input[["fileinput"]]$datapath[w],1)
} else {
mat<-data.frame(read_xls(input[["fileinput"]]$datapath[w],col_names = TRUE,col_types = "text"))
}
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector_2 <- as.vector(do.call(cbind, mat_list))
samples_failed<-grep("FALSE", mat_vector_2)
} else {
mat_vector_2<-c("mat_vector_2")
samples_failed<-NULL
}
}
if (input$datatype=="VCF" & length(grep(".vcf",input[["fileinput"]]$datapath))!=length(input[["fileinput"]]$datapath)){
aux<-grep(".vcf",input[["fileinput"]]$datapath,invert=TRUE)
samples_bad_format<-paste(input[["fileinput"]]$name[aux], collapse=" | ")
} else {
if (input$datatype=="TSV" & (length(grep(".tsv",input[["fileinput"]]$datapath)) + length(grep(".txt",input[["fileinput"]]$datapath))) != length(input[["fileinput"]]$datapath)){
aux1<-grep(".tsv",input[["fileinput"]]$datapath,invert=TRUE)
aux2<-input[["fileinput"]]$name[aux1]
aux3<-grep(".txt",aux2,invert=TRUE)
samples_bad_format<-paste(aux2[aux3], collapse=" | ")
}
else{
if (input$datatype=="Excel" & length(grep(".xls",input[["fileinput"]]$datapath)) != length(input[["fileinput"]]$datapath)){
aux<-grep(".xls",input[["fileinput"]]$datapath,invert=TRUE)
samples_bad_format<-paste(input[["fileinput"]]$name[aux], collapse=" | ")
} else {
if (input$datatype=="MAF" & length(grep(".maf",input[["fileinput"]]$datapath))<=0){
aux<-grep(".maf",input[["fileinput"]]$datapath,invert=TRUE)
samples_bad_format<-paste(input[["fileinput"]]$name[aux], collapse=" | ")
} else {
samples_bad_format<-NULL
}
}
}
}
tags$style(HTML(paste(".shiny-output-error-formats {visibility: hidden;}
.shiny-output-error-formats:before {visibility: visible; color: orangered; content:'Format error in uploaded file/s: ", samples_bad_format," Please select the correct input file format before uploading your file/s.';}
.shiny-output-error-maf {visibility: hidden;}
.shiny-output-error-maf:before {visibility: visible; color: orangered; content:'Only one multi-sample MAF file is allowed.';}
.shiny-output-error-noheader {visibility: hidden;}
.shiny-output-error-noheader:before {visibility: visible; color: orangered; content:'Uploaded file/s: ", paste(input[["fileinput"]]$name[samples_failed],collapse=" | ")," do not have the mandatory header with columns CHROM, POS, REF and ALT.';}
.shiny-output-error-numberofsamples {visibility: hidden;}
.shiny-output-error-numberofsamples:before {visibility: visible; color: orangered; content:'Too many samples for the online version of MuSiCa. Please, reduce the number of samples to 20 or download local version.';}")
)
)
})
################################################################
#Plot selectize to select samples to plot.
output$selected_samples<-renderUI({
#Error managemente for file format
error_message<-"File format error, please select the correct input file format before uploading your file/s."
if (input$datatype=="VCF"){
validate(
need(length(grep(".vcf",input[["fileinput"]]$datapath))==length(input[["fileinput"]]$datapath),error_message), errorClass = "formats"
)
#validate(
# need(length(input[["fileinput"]]$datapath)<=20,"samplenumber"),errorClass="numberofsamples"
# )
}
if (input$datatype=="TSV"){
validate(
need((length(grep(".tsv",input[["fileinput"]]$datapath)) + length(grep(".txt",input[["fileinput"]]$datapath))) == length(input[["fileinput"]]$datapath),error_message), errorClass = "formats"
)
#validate(
# need(length(input[["fileinput"]]$datapath)<=20,"samplenumber"),errorClass="numberofsamples"
# )
}
if (input$datatype=="TSV"){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
mat <- fread(input[["fileinput"]]$datapath[w],header=T,sep="\t",data.table=F)
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector <- as.vector(do.call(cbind, mat_list))
validate(
need(length(grep("FALSE", mat_vector))==0, "Uploaded files header do not have necessary columns CHROM, POS, REF and ALT."), errorClass = "noheader"
)
}
if (input$datatype=="Excel"){
validate(
need(length(grep(".xls",input[["fileinput"]]$datapath)) == length(input[["fileinput"]]$datapath),error_message), errorClass = "formats"
)
#validate(
# need(length(input[["fileinput"]]$datapath)<=20,"samplenumber"),errorClass="numberofsamples"
# )
}
if (input$datatype=="Excel"){
mat_list<-list()
for (w in 1:length(input[["fileinput"]]$datapath)){
if (length(grep(".xlsx",input[["fileinput"]]$datapath[w]))>0){
mat<-read.xlsx(input[["fileinput"]]$datapath[w],1)
} else {
mat<-data.frame(read_xls(input[["fileinput"]]$datapath[w],col_names = TRUE,col_types = "text"))
}
condition <- as.character(length(grep("TRUE", (c("CHROM", "POS", "REF", "ALT") %in% colnames(mat))))==4)
mat_list[[w]] <- condition
}
mat_vector <- as.vector(do.call(cbind, mat_list))
validate(
need(length(grep("FALSE", mat_vector))==0, "Uploaded files header do not have necessary columns CHROM, POS, REF and ALT."), errorClass = "noheader"
)
}
if (input$datatype=="MAF"){
validate(
need(length(grep(".maf",input[["fileinput"]]$datapath))>0,error_message),errorClass = "formats"
)
validate(
need(length(input[["fileinput"]]$datapath)==1, "Only one multi-sample MAF file is allowed."),errorClass = "maf"
)
}
if (length(vcfs())==1){
mysamp<-colnames(as.data.frame(fit_res()$contribution))
selectInput("mysamp","Select your samples",mysamp, multiple=TRUE, selectize = FALSE, size=1, selected=colnames(as.data.frame(fit_res()$contribution)))
} else {
if (input$tab=="reconst"){
mysamp<-colnames(as.data.frame(fit_res()$contribution))
selectInput("mysamp","Select your samples",mysamp, multiple=TRUE, selectize = FALSE, size=6, selected = mysamp[1])
} else {
mysamp<-c("All samples",colnames(as.data.frame(fit_res()$contribution)))
selectInput("mysamp","Select your samples",mysamp, multiple=TRUE, selectize = FALSE, size=6, selected="All samples")
}
}
})
output$selected_cancer_types<-renderUI({
if (input$tab=="comp_canc_sign"){
selectInput("mycancers","Select the cancers to compare", c("All cancers",colnames(read.table("./aux_files/cancermatrix.tsv",header=TRUE,sep="\t",row.names=1))), multiple=TRUE, selectize=FALSE, size=10, selected="All cancers")
}
})
#Checkbox to decide if mean is plotted.
output$mean_checkbox<-renderUI({
if (input$tab=="smp" | input$tab=="contrib" | input$tab=="comp_canc_sign"){
checkboxInput("meancheck","Show mean",value=FALSE)
} else {
return(invisible(NULL))
}
})
#Select which samples use to plot.
my_contributions<- reactive({
#Error management
if (length(input$mysamp)==0) return(invisible(NULL))
if ("All samples" %in% input$mysamp){
if(input$meancheck==TRUE) {
aux<-divisionRel(as.data.frame(fit_res()$contribution))
con<-data.frame(aux, mean = apply(aux,1,mean))
colnames(con)<-c(colnames(aux),"mean")
} else {
aux<-divisionRel(as.data.frame(fit_res()$contribution))
con<-data.frame(aux)
colnames(con)<-c(colnames(aux))
}
} else {
if(input$meancheck==TRUE) {
aux<-divisionRel(as.data.frame(fit_res()$contribution[,input$mysamp]))
con<-data.frame(aux, mean = apply(aux,1,mean))
colnames(con)<-c(colnames(aux),"mean")
} else {
aux<-divisionRel(as.data.frame(fit_res()$contribution[,input$mysamp]))
con<-data.frame(aux)
colnames(con)<-colnames(aux)
}
}
#Fixing colname of one sample (without mean)
if (ncol(con)==1){
colnames(con)<-setdiff(input$mysamp,c("All samples"))
}
#Fixing colname of one sample (with mean)
if (ncol(con)==1 & input$meancheck==TRUE & input$mysamp!="All samples"){
colnames(con)[1]<-setdiff(input$mysamp,c("All samples"))
}
return(con)
})
################################################################################
################################################################################
################################################################################
#######################################
#PLOT Somatic Mutation Prevalence (number mutations per megabase)
#######################################
#Selection of type of study and MB affected by it
output$kb_sequenced<-renderUI({
if (input$studytype == "Targeted Sequencing"){
numericInput("bases_sequenced","Kilobases sequenced",value = 10,min = 1)
}
})
#According to Alexandrov et al. 2013
megabases<-reactive({
if (input$studytype == "Whole Genome Sequencing"){
return(2800)
}
if (input$studytype == "Whole Exome Sequencing"){
return(30)
}
if (input$studytype == "Targeted Sequencing"){
return(input$bases_sequenced/1000)
}
})
#Selection of samples to plot
mutation_counts<- reactive({
if ("All samples" %in% input$mysamp){
if(input$meancheck==TRUE) {
mc<-data.frame(samples=c(names(vcfs()),"mean"),smp=(c(sapply(vcfs(),length),mean(sapply(vcfs(),length))))/megabases())
} else {
mc<-data.frame(samples=names(vcfs()),smp=(sapply(vcfs(),length))/megabases())
}
} else {
if(input$meancheck==TRUE) {
aux<-input$mysamp
mc<-data.frame(samples=c(names(vcfs()[aux]),"mean"), smp=(c(sapply(vcfs()[aux],length),mean(sapply(vcfs()[aux],length))))/megabases())
} else {
mc<-data.frame(samples=names(vcfs()[input$mysamp]), smp=(sapply(vcfs()[input$mysamp],length))/megabases())
}
}
return(mc)
})
# PLOT somatic mutation prevalence
output$smp <- renderPlot({
#Error management
if (length(input$mysamp)==0) return(invisible(NULL))
mutation_counts_new<-data.frame(samples=mutation_counts()$samples,smp=round(mutation_counts()$smp,1))
plot_smp<-ggplot(data=mutation_counts_new,aes(x=samples,y=smp))
plot_smp + geom_bar(stat="identity",fill="orangered2") + theme_minimal() + geom_text(aes(label=smp), size=5, position = position_stack(vjust = 0.5), colour="white") + coord_flip() + labs(x = "", y = "Somatic mutation prevalence (number of mutations per megabase)") + theme(axis.text=element_text(size=12), axis.title = element_text(size = 13, face = "bold"), panel.grid.major.y=element_blank(), panel.grid.minor.y=element_blank(), panel.grid.major.x=element_blank(), panel.grid.minor.x=element_blank())
}, height = function(x=length(colnames(my_contributions())) ){ if (x < 5) {return(250)}
if (x >100) {return(10000)}
if (x<=100 & x>=5) {return (50*x)}
})
#Download Plot somatic mutation prevalence
output$download_smp_plot <- downloadHandler (
filename = function(){
paste("mut_prevalence_plot",input$type_smp_plot, sep=".")
},
content = function(ff) {
mutation_counts_new<-data.frame(samples=mutation_counts()$samples,smp=round(mutation_counts()$smp,1))
plot_smp<-ggplot(data=mutation_counts_new,aes(x=samples,y=smp))
plot_smp + geom_bar(stat="identity",fill="orangered2") + theme_minimal() + geom_text(aes(label=smp), size=5, position = position_stack(vjust = 0.5), colour="white") + coord_flip() + labs(x = "", y = "Somatic mutation prevalence (number of mutations per megabase)") + theme(axis.text=element_text(size=12), axis.title = element_text(size = 13, face = "bold"), panel.grid.major.y=element_blank(), panel.grid.minor.y=element_blank(), panel.grid.major.x=element_blank(), panel.grid.minor.x=element_blank())
ggsave(ff,height=min(2*nrow(mutation_counts_new),40),width=25,dpi=ppi,units="cm")
}
)
#Download Table somatic mutation prevalence
output$download_smp_table <- downloadHandler(
filename="Somatic_mut_prev.txt",
content=function (file){
mutation_counts_new<-data.frame(Sample=mutation_counts()$samples,Somatic_Mutation_Prevalence=round(mutation_counts()$smp,1),Number_of_Samples=length(names(vcfs())))
write.table(x = mutation_counts_new, file = file, sep = "\t", quote=F, row.names=F)
}
)
#######################################
#PLOT 96 nucleotide changes profile (samples individually)
#######################################
#Plot 96 profile
output$prof96 <- renderPlot({
aux_96_profile<-as.matrix(mut_mat()[,setdiff(colnames(my_contributions()),c("mean"))])
colnames(aux_96_profile)<-setdiff(colnames(my_contributions()),c("mean"))
aux_ymax<-as.data.frame(aux_96_profile)
rownames(aux_ymax)<-1:96
max_ymax<-max(divisionRel(aux_ymax))
plot_96_profile(aux_96_profile,ymax = max_ymax) + scale_y_continuous(breaks = seq(0, max_ymax, 0.05))
}, height = function(x=length(colnames(my_contributions())) ){ if (x < 5) {return(400)}
if (x >100) {return(10000)}
if (x<=100 & x>=5) {return (20+100*x)}
}
)
#Download Plot 96 profile
output$download_prof96_plot <- downloadHandler (
filename = function(){
paste("prof96_plot",input$type_prof96_plot, sep=".")
},
content = function(ff) {
aux_96_profile<-as.matrix(mut_mat()[,setdiff(colnames(my_contributions()),c("mean"))])
colnames(aux_96_profile)<-setdiff(colnames(my_contributions()),c("mean"))
aux_ymax<-as.data.frame(aux_96_profile)
rownames(aux_ymax)<-1:96
max_ymax<-max(divisionRel(aux_ymax))
plot_96_profile(aux_96_profile,ymax = max_ymax) + scale_y_continuous(breaks = seq(0, max_ymax, 0.05))
ggsave(ff,height=min(4*ncol(aux_96_profile),40),width=25,dpi=ppi,units="cm")
}
)
#Download Plot 96 TABLE
output$download_prof96_table <- downloadHandler(
filename="Mutational_Profile.txt",
content=function (file){
aux_96_profile<-as.matrix(mut_mat()[,setdiff(colnames(my_contributions()),c("mean"))])
aux_ymax<-divisionRel(as.data.frame(aux_96_profile))
write.table(x = data.frame(Substitution_Type = cancer_signatures_mut_types[,1], Trinucleotide = cancer_signatures_mut_types[,2], Somatic_Mutation_Type = cancer_signatures_mut_types[,3], aux_ymax), file = file, sep = "\t", quote=F, row.names=F)
}
)
#######################################
### Plot heatmap with contributions
#######################################
#DataTable
output$contr <- renderDataTable({
data.frame(Signature = 1:30, Proposed_Etiology = proposed_etiology, round(my_contributions(),3))
},
options = list(lengthChange=FALSE,pageLength=30, paging=FALSE, searching=FALSE, info=FALSE)
)
#Download Table
output$download_contr <- downloadHandler( filename="COSMIC_sign_contributions.txt", content=function (file){
write.table(x = data.frame(Signature = 1:30, Proposed_Etiology = proposed_etiology, round(my_contributions(),3)), file = file, sep = "\t", quote=F, row.names=F)})
#check if column or row dendogram is needed
output$row_dendro_heatmap<-renderUI({
if (input$tab == "contrib"){
radioButtons("row_d_heatmap", "Row dendrogram", c("yes","no"),selected = "no",inline = TRUE)
} else {
return(invisible(NULL))
}
})
output$col_dendro_heatmap<-renderUI({
if (input$tab == "contrib"){
radioButtons("col_d_heatmap", "Column dendrogram", c("yes","no"),selected = "no",inline = TRUE)
} else {
return(invisible(NULL))
}
})
#check if sample names are needed
output$heatmap_sample_names<-renderUI({
if (input$tab == "contrib" | input$tab == "comp_canc_sign"){
checkboxInput("samplenames","Show sample names",value=TRUE)
} else {
return(invisible(NULL))
}
})
#HeatMap
output$heatmap_signatures <- renderPlotly({
if (length(input$row_d_heatmap)==0) return(invisible(NULL))
if (length(input$col_d_heatmap)==0) return(invisible(NULL))
a<-my_contributions()
if (ncol(a)==1) colnames(a)<-colnames(my_contributions()) ## fix colnames when there is only one sample
rownames(a)<-colnames(cancer_signatures)[1:30]
colorends <- c("white","red")
dendro <- "none"
if (input$row_d_heatmap=="yes") dendro<-"row"
if (input$col_d_heatmap=="yes") dendro<-"column"
if (input$row_d_heatmap=="yes" & input$col_d_heatmap=="yes") dendro<-"both"
if (input$samplenames==FALSE){
colnames(a)<-1:length(colnames(a))
}
heatmaply(a, scale_fill_gradient_fun = scale_fill_gradientn(colours = colorends, limits = c(0,1)),
dendrogram = dendro, k_row = 1, k_col = 1, column_text_angle = 90, distfun = 'pearson' )
})
#Download HeatMap
output$download_signatures_plot <- downloadHandler (
filename = function(){paste("signatures_plot",input$type_signatures_plot, sep=".")},
content = function(ff) {
a<-my_contributions()
if (ncol(a)==1) colnames(a)<-colnames(my_contributions()) ## fix colnames when there is only one sample
rownames(a)<-colnames(cancer_signatures)[1:30]
colorends <- c("white","red")
dendro <- "none"
if (input$row_d_heatmap=="yes") dendro<-"row"
if (input$col_d_heatmap=="yes") dendro<-"column"
if (input$row_d_heatmap=="yes" & input$col_d_heatmap=="yes") dendro<-"both"
if (input$samplenames==FALSE){
colnames(a)<-1:length(colnames(a))
}
heatmaply(a, scale_fill_gradient_fun = scale_fill_gradientn(colours = colorends, limits = c(0,1)),
dendrogram = dendro, k_row = 1, k_col = 1, column_text_angle = 90, distfun = 'pearson',
file = ff)
})
#######################################
### PLOT Reconstructed Mutational Profile
#######################################
#Plot reconstructed profile
output$reconst <- renderPlot({
validate(
need(length(input$mysamp)==1,"Sample selection error, please select just one sample at a time to visualize its reconstructed mutational profile."), errorClass = "reconstructed"
)
if (input$mysamp=="All samples") return(invisible(NULL))
original_prof <- mut_mat()[,input$mysamp]
reconstructed_prof <- fit_res()$reconstructed[,input$mysamp]
aux_ymax<-data.frame(original_prof,reconstructed_prof)
max_ymax<-max(divisionRel(aux_ymax))
plot_compare_profiles(original_prof,reconstructed_prof,profile_names=c("Original","Reconstructed"),profile_ymax = max_ymax)
})
#Download Plot reconstructed profile
output$download_reconst_plot <- downloadHandler (
filename = function(){
paste("reconstructed_plot",input$type_reconst_plot, sep=".")
},
content = function(ff) {
original_prof <- mut_mat()[,input$mysamp]
reconstructed_prof <- fit_res()$reconstructed[,input$mysamp]
aux_ymax<-data.frame(original_prof,reconstructed_prof)
max_ymax<-max(divisionRel(aux_ymax))
plot_compare_profiles(original_prof,reconstructed_prof,profile_names=c("Original","Reconstructed"),profile_ymax = max_ymax)
ggsave(ff,height=6,width=10,dpi=ppi)
}
)
#Download reconstructed TABLE
output$download_reconst_table <- downloadHandler(
filename="reconstructed_table.txt",
content=function (file){
original_prof <- mut_mat()[,input$mysamp]
reconstructed_prof <- fit_res()$reconstructed[,input$mysamp]
aux_ymax<-divisionRel(data.frame(Original_Profile = original_prof, Reconstructed_Profile = reconstructed_prof))
write.table(x = data.frame(Substitution_Type = cancer_signatures_mut_types[,1], Trinucleotide = cancer_signatures_mut_types[,2], Somatic_Mutation_Type = cancer_signatures_mut_types[,3], aux_ymax), file = file, sep = "\t", quote=F, row.names=F)
}
)
#######################################
### Plot - Comparison with other cancers
#######################################
#check if column or row dendogram is needed
output$row_dendro_cancers<-renderUI({
if (input$tab == "comp_canc_sign"){
radioButtons("row_c_heatmap", "Row dendrogram", c("yes","no"),selected = "no",inline = TRUE)
} else {
return(invisible(NULL))
}
})
output$col_dendro_cancers<-renderUI({
if (input$tab == "comp_canc_sign"){
radioButtons("col_c_heatmap", "Column dendrogram", c("yes","no"),selected = "no",inline = TRUE)
} else {
return(invisible(NULL))
}
})
#HeatMap
output$heatmap_known <- renderPlotly({
if (length(input$mysamp)==0) return(invisible(NULL))
if (length(input$mycancers)==0) return(invisible(NULL))
if ("All cancers" %in% input$mycancers) my.sel.cancers<-colnames(known_cancer_signatures)
else my.sel.cancers<-intersect(input$mycancers,colnames(known_cancer_signatures))
a<-data.frame(my_contributions()[1:30,], known_cancer_signatures[1:30,my.sel.cancers])
rownames(a)<-colnames(cancer_signatures)[1:30]
if (ncol(my_contributions())==1) colnames(a)[1]<-colnames(my_contributions()) ## fix colnames when there is only one sample
if (length(my.sel.cancers)==1) colnames(a)[length(colnames(a))]<-my.sel.cancers ## fix colnames when there is only one cancer type
for (i in 1:(ncol(a)-length(my.sel.cancers))) {
#a[,i]<-a[,i]/max(a[,i]) # don't do a rescaling
a[,i]<-a[,i]/sum(a[,i]) # put the proportions
}
for (i in (ncol(a)-length(my.sel.cancers)+1):ncol(a)) {
a[,i]<-a[,i]*2.5 # put the proportions # 1 goes to 2.5 (light blue)
}
rownames(a)<-colnames(cancer_signatures)[1:30]
colorends <- c("white","red", "white", "blue")
dendro <- "none"
if (input$row_c_heatmap=="yes") dendro<-"row"
if (input$col_c_heatmap=="yes") dendro<-"column"
if (input$row_c_heatmap=="yes" & input$col_c_heatmap=="yes") dendro<-"both"
if (input$samplenames==FALSE){
colnames(a)<-1:length(colnames(a))
}
heatmaply(a, scale_fill_gradient_fun = scale_fill_gradientn(colours = colorends, limits = c(0,3)),
dendrogram = dendro, k_row = 1, k_col = 1, column_text_angle = 90, hide_colorbar = TRUE,
distfun = 'pearson')
})
# Download HeatMap
output$download_known_plot <- downloadHandler(filename = function(){paste("comparison_with_cancers",input$type_known_plot, sep=".")}, content=function (ff) {
if ("All cancers" %in% input$mycancers) my.sel.cancers<-colnames(known_cancer_signatures)
else my.sel.cancers<-intersect(input$mycancers,colnames(known_cancer_signatures))
a<-data.frame(my_contributions()[1:30,], known_cancer_signatures[1:30,my.sel.cancers])
rownames(a)<-colnames(cancer_signatures)[1:30]
if (ncol(my_contributions())==1) colnames(a)[1]<-colnames(my_contributions()) ## fix colnames when there is only one sample
if (length(my.sel.cancers)==1) colnames(a)[length(colnames(a))]<-my.sel.cancers ## fix colnames when there is only one cancer type
for (i in 1:(ncol(a)-length(my.sel.cancers))) {
#a[,i]<-a[,i]/max(a[,i]) # don't do a rescaling
a[,i]<-a[,i]/sum(a[,i]) # put the proportions
}
for (i in (ncol(a)-length(my.sel.cancers)+1):ncol(a)) {
a[,i]<-a[,i]+1.5 # put the proportions # add 1.5 to cancers