Mercurial > repos > davidvanzessen > argalaxy_tools
diff report_clonality/RScript.r~ @ 26:28fbbdfd7a87 draft
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| author | davidvanzessen |
|---|---|
| date | Mon, 13 Feb 2017 09:08:46 -0500 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/report_clonality/RScript.r~ Mon Feb 13 09:08:46 2017 -0500 @@ -0,0 +1,658 @@ +# ---------------------- load/install packages ---------------------- + +if (!("gridExtra" %in% rownames(installed.packages()))) { + install.packages("gridExtra", repos="http://cran.xl-mirror.nl/") +} +library(gridExtra) +if (!("ggplot2" %in% rownames(installed.packages()))) { + install.packages("ggplot2", repos="http://cran.xl-mirror.nl/") +} +library(ggplot2) +if (!("plyr" %in% rownames(installed.packages()))) { + install.packages("plyr", repos="http://cran.xl-mirror.nl/") +} +library(plyr) + +if (!("data.table" %in% rownames(installed.packages()))) { + install.packages("data.table", repos="http://cran.xl-mirror.nl/") +} +library(data.table) + +if (!("reshape2" %in% rownames(installed.packages()))) { + install.packages("reshape2", repos="http://cran.xl-mirror.nl/") +} +library(reshape2) + +if (!("lymphclon" %in% rownames(installed.packages()))) { + install.packages("lymphclon", repos="http://cran.xl-mirror.nl/") +} +library(lymphclon) + +# ---------------------- parameters ---------------------- + +args <- commandArgs(trailingOnly = TRUE) + +infile = args[1] #path to input file +outfile = args[2] #path to output file +outdir = args[3] #path to output folder (html/images/data) +clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering +ct = unlist(strsplit(clonaltype, ",")) +species = args[5] #human or mouse +locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD +filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no) +clonality_method = args[8] + +# ---------------------- Data preperation ---------------------- + +inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="") + +setwd(outdir) + +# remove weird rows +inputdata = inputdata[inputdata$Sample != "",] + +#remove the allele from the V,D and J genes +inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene) +inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene) +inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene) + +inputdata$clonaltype = 1:nrow(inputdata) + +PRODF = inputdata +UNPROD = inputdata +if(filterproductive){ + if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column + PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ] + UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ] + } else { + PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ] + UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ] + } +} + +clonalityFrame = PRODF + +#remove duplicates based on the clonaltype +if(clonaltype != "none"){ + clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples + PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":")) + PRODF = PRODF[!duplicated(PRODF$clonaltype), ] + + UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":")) + UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ] + + #again for clonalityFrame but with sample+replicate + clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":")) + clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":")) + clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ] +} + +PRODF$freq = 1 + +if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*" + PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID) + PRODF$freq = gsub("_.*", "", PRODF$freq) + PRODF$freq = as.numeric(PRODF$freq) + if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence + PRODF$freq = 1 + } +} + + + +#write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive +write.table(PRODF, "allUnique.txt", sep=",",quote=F,row.names=F,col.names=T) +write.table(PRODF, "allUnique.csv", sep="\t",quote=F,row.names=F,col.names=T) +write.table(UNPROD, "allUnproductive.csv", sep=",",quote=F,row.names=F,col.names=T) + +#write the samples to a file +sampleFile <- file("samples.txt") +un = unique(inputdata$Sample) +un = paste(un, sep="\n") +writeLines(un, sampleFile) +close(sampleFile) + +# ---------------------- Counting the productive/unproductive and unique sequences ---------------------- + +if(!("Functionality" %in% inputdata)){ #add a functionality column to the igblast data + inputdata$Functionality = "unproductive" + search = (inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND") + if(sum(search) > 0){ + inputdata[search,]$Functionality = "productive" + } +} + +inputdata.dt = data.table(inputdata) #for speed + +if(clonaltype == "none"){ + ct = c("clonaltype") +} + +inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_") +samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample))) +frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)]) + + +sample_productive_count = inputdata.dt[, list(All=.N, + Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), + perc_prod = 1, + Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), + perc_prod_un = 1, + Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]), + perc_unprod = 1, + Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]), + perc_unprod_un = 1), + by=c("Sample")] + +sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100) +sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100) + +sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100) +sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100) + + +sample_replicate_productive_count = inputdata.dt[, list(All=.N, + Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), + perc_prod = 1, + Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), + perc_prod_un = 1, + Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]), + perc_unprod = 1, + Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]), + perc_unprod_un = 1), + by=c("samples_replicates")] + +sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100) +sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100) + +sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100) +sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100) + +setnames(sample_replicate_productive_count, colnames(sample_productive_count)) + +counts = rbind(sample_replicate_productive_count, sample_productive_count) +counts = counts[order(counts$Sample),] + +write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F) + +# ---------------------- Frequency calculation for V, D and J ---------------------- + +PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")]) +Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length))) +PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE) +PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total)) + +PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")]) +Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length))) +PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE) +PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total)) + +PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")]) +Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length))) +PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE) +PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total)) + +# ---------------------- Setting up the gene names for the different species/loci ---------------------- + +Vchain = "" +Dchain = "" +Jchain = "" + +if(species == "custom"){ + print("Custom genes: ") + splt = unlist(strsplit(locus, ";")) + print(paste("V:", splt[1])) + print(paste("D:", splt[2])) + print(paste("J:", splt[3])) + + Vchain = unlist(strsplit(splt[1], ",")) + Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain)) + + Dchain = unlist(strsplit(splt[2], ",")) + if(length(Dchain) > 0){ + Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain)) + } else { + Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0)) + } + + Jchain = unlist(strsplit(splt[3], ",")) + Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain)) + +} else { + genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="") + + Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")] + colnames(Vchain) = c("v.name", "chr.orderV") + Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")] + colnames(Dchain) = c("v.name", "chr.orderD") + Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")] + colnames(Jchain) = c("v.name", "chr.orderJ") +} +useD = TRUE +if(nrow(Dchain) == 0){ + useD = FALSE + cat("No D Genes in this species/locus") +} +print(paste("useD:", useD)) + +# ---------------------- merge with the frequency count ---------------------- + +PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE) + +PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE) + +PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE) + +# ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ---------------------- + +pV = ggplot(PRODFV) +pV = pV + geom_bar( aes( x=factor(reorder(Top.V.Gene, chr.orderV)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage") +write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + +png("VPlot.png",width = 1280, height = 720) +pV +dev.off(); + +if(useD){ + pD = ggplot(PRODFD) + pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage") + write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + + png("DPlot.png",width = 800, height = 600) + print(pD) + dev.off(); +} + +pJ = ggplot(PRODFJ) +pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage") +write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + +png("JPlot.png",width = 800, height = 600) +pJ +dev.off(); + +pJ = ggplot(PRODFJ) +pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage") +write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + +png("JPlot.png",width = 800, height = 600) +pJ +dev.off(); + +# ---------------------- Now the frequency plots of the V, D and J families ---------------------- + +VGenes = PRODF[,c("Sample", "Top.V.Gene")] +VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene) +VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")]) +TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample]) +VGenes = merge(VGenes, TotalPerSample, by="Sample") +VGenes$Frequency = VGenes$Count * 100 / VGenes$total +VPlot = ggplot(VGenes) +VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Distribution of V gene families") + + ylab("Percentage of sequences") +png("VFPlot.png") +VPlot +dev.off(); +write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + +if(useD){ + DGenes = PRODF[,c("Sample", "Top.D.Gene")] + DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene) + DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")]) + TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample]) + DGenes = merge(DGenes, TotalPerSample, by="Sample") + DGenes$Frequency = DGenes$Count * 100 / DGenes$total + DPlot = ggplot(DGenes) + DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Distribution of D gene families") + + ylab("Percentage of sequences") + png("DFPlot.png") + print(DPlot) + dev.off(); + write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) +} + +JGenes = PRODF[,c("Sample", "Top.J.Gene")] +JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene) +JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")]) +TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample]) +JGenes = merge(JGenes, TotalPerSample, by="Sample") +JGenes$Frequency = JGenes$Count * 100 / JGenes$total +JPlot = ggplot(JGenes) +JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Distribution of J gene families") + + ylab("Percentage of sequences") +png("JFPlot.png") +JPlot +dev.off(); +write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) + +# ---------------------- Plotting the cdr3 length ---------------------- + +CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")]) +TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample]) +CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample") +CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total +CDR3LengthPlot = ggplot(CDR3Length) +CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Length distribution of CDR3") + + xlab("CDR3 Length") + + ylab("Percentage of sequences") +png("CDR3LengthPlot.png",width = 1280, height = 720) +CDR3LengthPlot +dev.off() +write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T) + +# ---------------------- Plot the heatmaps ---------------------- + + +#get the reverse order for the V and D genes +revVchain = Vchain +revDchain = Dchain +revVchain$chr.orderV = rev(revVchain$chr.orderV) +revDchain$chr.orderD = rev(revDchain$chr.orderD) + +if(useD){ + plotVD <- function(dat){ + if(length(dat[,1]) == 0){ + return() + } + img = ggplot() + + geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_gradient(low="gold", high="blue", na.value="white") + + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + + xlab("D genes") + + ylab("V Genes") + + png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name))) + print(img) + dev.off() + write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) + } + + VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")]) + + VandDCount$l = log(VandDCount$Length) + maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")]) + VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T) + VandDCount$relLength = VandDCount$l / VandDCount$max + + cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample)) + + completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE) + completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) + completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) + VDList = split(completeVD, f=completeVD[,"Sample"]) + + lapply(VDList, FUN=plotVD) +} + +plotVJ <- function(dat){ + if(length(dat[,1]) == 0){ + return() + } + cat(paste(unique(dat[3])[1,1])) + img = ggplot() + + geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_gradient(low="gold", high="blue", na.value="white") + + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + + xlab("J genes") + + ylab("V Genes") + + png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name))) + print(img) + dev.off() + write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) +} + +VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")]) + +VandJCount$l = log(VandJCount$Length) +maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")]) +VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T) +VandJCount$relLength = VandJCount$l / VandJCount$max + +cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample)) + +completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE) +completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) +completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) +VJList = split(completeVJ, f=completeVJ[,"Sample"]) +lapply(VJList, FUN=plotVJ) + +if(useD){ + plotDJ <- function(dat){ + if(length(dat[,1]) == 0){ + return() + } + img = ggplot() + + geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_gradient(low="gold", high="blue", na.value="white") + + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + + xlab("J genes") + + ylab("D Genes") + + png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name))) + print(img) + dev.off() + write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) + } + + + DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")]) + + DandJCount$l = log(DandJCount$Length) + maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")]) + DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T) + DandJCount$relLength = DandJCount$l / DandJCount$max + + cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample)) + + completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE) + completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) + completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) + DJList = split(completeDJ, f=completeDJ[,"Sample"]) + lapply(DJList, FUN=plotDJ) +} + + +# ---------------------- calculating the clonality score ---------------------- + +if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available +{ + if(clonality_method == "boyd"){ + samples = split(clonalityFrame, clonalityFrame$Sample, drop=T) + + for (sample in samples){ + res = data.frame(paste=character(0)) + sample_id = unique(sample$Sample)[[1]] + for(replicate in unique(sample$Replicate)){ + tmp = sample[sample$Replicate == replicate,] + clone_table = data.frame(table(tmp$clonaltype)) + clone_col_name = paste("V", replicate, sep="") + colnames(clone_table) = c("paste", clone_col_name) + res = merge(res, clone_table, by="paste", all=T) + } + + res[is.na(res)] = 0 + infer.result = infer.clonality(as.matrix(res[,2:ncol(res)])) + + write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F) + + res$type = rowSums(res[,2:ncol(res)]) + + coincidence.table = data.frame(table(res$type)) + colnames(coincidence.table) = c("Coincidence Type", "Raw Coincidence Freq") + write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T) + } + } else { + write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T) + + clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")]) + clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")]) + clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count + clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")]) + clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample") + + ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15') + tcct = textConnection(ct) + CT = read.table(tcct, sep="\t", header=TRUE) + close(tcct) + clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T) + clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight + + ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")]) + ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")]) + clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads) + ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads + + ReplicatePrint <- function(dat){ + write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) + } + + ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) + lapply(ReplicateSplit, FUN=ReplicatePrint) + + ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")]) + clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) + + ReplicateSumPrint <- function(dat){ + write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) + } + + ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) + lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) + + clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")]) + clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T) + clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow + clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2) + clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1) + + ClonalityScorePrint <- function(dat){ + write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) + } + + clonalityScore = clonalFreqCount[c("Sample", "Result")] + clonalityScore = unique(clonalityScore) + + clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"]) + lapply(clonalityScoreSplit, FUN=ClonalityScorePrint) + + clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")] + + + + ClonalityOverviewPrint <- function(dat){ + write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) + } + + clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample) + lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint) + } +} + +imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb") +if(all(imgtcolumns %in% colnames(inputdata))) +{ + print("found IMGT columns, running junction analysis") + newData = data.frame(data.table(PRODF)[,list(unique=.N, + VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=mean(.SD$P3V.nt.nb, na.rm=T), + N1=mean(.SD$N1.REGION.nt.nb, na.rm=T), + P2=mean(.SD$P5D.nt.nb, na.rm=T), + DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), + DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), + P3=mean(.SD$P3D.nt.nb, na.rm=T), + N2=mean(.SD$N2.REGION.nt.nb, na.rm=T), + P4=mean(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=( mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)), + + Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) + + mean(.SD$N2.REGION.nt.nb, na.rm=T)), + + Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) + + mean(.SD$P5D.nt.nb, na.rm=T) + + mean(.SD$P3D.nt.nb, na.rm=T) + + mean(.SD$P5J.nt.nb, na.rm=T))), + by=c("Sample")]) + print(newData) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(UNPROD)[,list(unique=.N, + VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=mean(.SD$P3V.nt.nb, na.rm=T), + N1=mean(.SD$N1.REGION.nt.nb, na.rm=T), + P2=mean(.SD$P5D.nt.nb, na.rm=T), + DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), + DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), + P3=mean(.SD$P3D.nt.nb, na.rm=T), + N2=mean(.SD$N2.REGION.nt.nb, na.rm=T), + P4=mean(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=(mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) + + mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)), + Total.N=( mean(.SD$N1.REGION.nt.nb, na.rm=T) + + mean(.SD$N2.REGION.nt.nb, na.rm=T)), + Total.P=( mean(.SD$P3V.nt.nb, na.rm=T) + + mean(.SD$P5D.nt.nb, na.rm=T) + + mean(.SD$P3D.nt.nb, na.rm=T) + + mean(.SD$P5J.nt.nb, na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) +} + +# ---------------------- AA composition in CDR3 ---------------------- + +AACDR3 = PRODF[,c("Sample", "CDR3.Seq")] + +TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample]) + +AAfreq = list() + +for(i in 1:nrow(TotalPerSample)){ + sample = TotalPerSample$Sample[i] + AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), "")))) + AAfreq[[i]]$Sample = sample +} + +AAfreq = ldply(AAfreq, data.frame) +AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T) +AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100) + + +AAorder = read.table(sep="\t", header=TRUE, text="order.aa\tAA\n1\tR\n2\tK\n3\tN\n4\tD\n5\tQ\n6\tE\n7\tH\n8\tP\n9\tY\n10\tW\n11\tS\n12\tT\n13\tG\n14\tA\n15\tM\n16\tC\n17\tF\n18\tL\n19\tV\n20\tI") +AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE) + +AAfreq = AAfreq[!is.na(AAfreq$order.aa),] + +AAfreqplot = ggplot(AAfreq) +AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' ) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) +AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage") + +png("AAComposition.png",width = 1280, height = 720) +AAfreqplot +dev.off() +write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T) + +