Mercurial > repos > davidvanzessen > argalaxy_tools
diff report_clonality/RScript.r @ 52:124b7fd92a3e draft
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| author | davidvanzessen |
|---|---|
| date | Thu, 25 Feb 2021 13:36:15 +0000 |
| parents | 1d8728f3ff37 |
| children | 81b3eb11ed2c |
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--- a/report_clonality/RScript.r Thu Aug 08 07:40:36 2019 -0400 +++ b/report_clonality/RScript.r Thu Feb 25 13:36:15 2021 +0000 @@ -1,1044 +1,1044 @@ -# ---------------------- 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 ---------------------- - -print("Report Clonality - Data preperation") - -inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="", stringsAsFactors=F) - -inputdata$Sample = as.character(inputdata$Sample) - - -print(paste("nrows: ", nrow(inputdata))) - -setwd(outdir) - -# remove weird rows -inputdata = inputdata[inputdata$Sample != "",] - -print(paste("nrows: ", nrow(inputdata))) - -#remove the allele from the V,D and J genes -inputdata$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$Top.J.Gene) - -print(paste("nrows: ", nrow(inputdata))) - -#filter uniques -inputdata.removed = inputdata[NULL,] - -print(paste("nrows: ", nrow(inputdata))) - -inputdata$clonaltype = 1:nrow(inputdata) - -#keep track of the count of sequences in samples or samples/replicates for the front page overview -input.sample.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample")]) -input.rep.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample", "Replicate")]) - -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)", ] - PRODF = inputdata[inputdata$Functionality %in% c("productive (see comment)","productive"),] - - PRODF.count = data.frame(data.table(PRODF)[, list(count=.N), by=c("Sample")]) - - UNPROD = inputdata[inputdata$Functionality %in% c("unproductive (see comment)","unproductive"), ] - } 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" ), ] - } -} - -for(i in 1:nrow(UNPROD)){ - if(!is.numeric(UNPROD[i,"CDR3.Length"])){ - UNPROD[i,"CDR3.Length"] = 0 - } -} - -prod.sample.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample")]) -prod.rep.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample", "Replicate")]) - -unprod.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample")]) -unprod.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample", "Replicate")]) - -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$clonality_clonaltype), ] -} - -if(nrow(PRODF) == 0){ - stop("No sequences left after filtering") -} - -prod.unique.sample.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample")]) -prod.unique.rep.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample", "Replicate")]) - -unprod.unique.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample")]) -unprod.unique.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample", "Replicate")]) - - -PRODF$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$freq = 1 - } -} - -#make a names list with sample -> color -naive.colors = c('blue4', 'darkred', 'olivedrab3', 'red', 'gray74', 'darkviolet', 'lightblue1', 'gold', 'chartreuse2', 'pink', 'Paleturquoise3', 'Chocolate1', 'Yellow', 'Deeppink3', 'Mediumorchid1', 'Darkgreen', 'Blue', 'Gray36', 'Hotpink', 'Yellow4') -unique.samples = unique(PRODF$Sample) - -if(length(unique.samples) <= length(naive.colors)){ - sample.colors = naive.colors[1:length(unique.samples)] -} else { - sample.colors = rainbow(length(unique.samples)) -} - -names(sample.colors) = unique.samples - -print("Sample.colors") -print(sample.colors) - - -#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="\t",quote=F,row.names=F,col.names=T) -#write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T) -write.table(UNPROD, "allUnproductive.txt", sep="\t",quote=F,row.names=F,col.names=T) - -print("SAMPLE TABLE:") -print(table(PRODF$Sample)) - -#write the samples to a file -sampleFile <- file("samples.txt") -un = unique(inputdata$Sample) -un = paste(un, sep="\n") -writeLines(un, sampleFile) -close(sampleFile) - -# ---------------------- Counting the productive/unproductive and unique sequences ---------------------- - -print("Report Clonality - counting productive/unproductive/unique") - -#create the table on the overview page with the productive/unique counts per sample/replicate -#first for sample - -sample.count = merge(input.sample.count, prod.sample.count, by="Sample", all.x=T) -sample.count$perc_prod = round(sample.count$Productive / sample.count$All * 100) -sample.count = merge(sample.count, prod.unique.sample.count, by="Sample", all.x=T) -sample.count$perc_prod_un = round(sample.count$Productive_unique / sample.count$All * 100) - -sample.count = merge(sample.count , unprod.sample.count, by="Sample", all.x=T) -sample.count$perc_unprod = round(sample.count$Unproductive / sample.count$All * 100) -sample.count = merge(sample.count, unprod.unique.sample.count, by="Sample", all.x=T) -sample.count$perc_unprod_un = round(sample.count$Unproductive_unique / sample.count$All * 100) - -#then sample/replicate -rep.count = merge(input.rep.count, prod.rep.count, by=c("Sample", "Replicate"), all.x=T) - -print(rep.count) - -fltr = is.na(rep.count$Productive) -if(any(fltr)){ - rep.count[fltr,"Productive"] = 0 -} - -print(rep.count) - -rep.count$perc_prod = round(rep.count$Productive / rep.count$All * 100) -rep.count = merge(rep.count, prod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T) -rep.count$perc_prod_un = round(rep.count$Productive_unique / rep.count$All * 100) - -rep.count = merge(rep.count, unprod.rep.count, by=c("Sample", "Replicate"), all.x=T) -rep.count$perc_unprod = round(rep.count$Unproductive / rep.count$All * 100) -rep.count = merge(rep.count, unprod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T) -rep.count$perc_unprod_un = round(rep.count$Unproductive_unique / rep.count$All * 100) - -rep.count$Sample = paste(rep.count$Sample, rep.count$Replicate, sep="_") -rep.count = rep.count[,names(rep.count) != "Replicate"] - -count = rbind(sample.count, rep.count) - - - -write.table(x=count, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F) - -# ---------------------- V+J+CDR3 sequence count ---------------------- - -VJCDR3.count = data.frame(table(clonalityFrame$Top.V.Gene, clonalityFrame$Top.J.Gene, clonalityFrame$CDR3.Seq.DNA)) -names(VJCDR3.count) = c("Top.V.Gene", "Top.J.Gene", "CDR3.Seq.DNA", "Count") - -VJCDR3.count = VJCDR3.count[VJCDR3.count$Count > 0,] -VJCDR3.count = VJCDR3.count[order(-VJCDR3.count$Count),] - -write.table(x=VJCDR3.count, file="VJCDR3_count.txt", sep="\t",quote=F,row.names=F,col.names=T) - -# ---------------------- Frequency calculation for V, D and J ---------------------- - -print("Report Clonality - frequency calculation V, D and J") - -PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")]) -Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$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 ---------------------- - -print("Report Clonality - getting genes for 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(nrow(Vchain), "genes in V")) -print(paste(nrow(Dchain), "genes in D")) -print(paste(nrow(Jchain), "genes in J")) - -# ---------------------- 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 ---------------------- - -print("Report Clonality - V, D and J frequency plots") - -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") + scale_fill_manual(values=sample.colors) -pV = pV + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) -write.table(x=PRODFV, file="VFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) - -png("VPlot.png",width = 1280, height = 720) -pV -dev.off() - -ggsave("VPlot.pdf", pV, width=13, height=7) - -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") + scale_fill_manual(values=sample.colors) - pD = pD + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) - write.table(x=PRODFD, file="DFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) - - png("DPlot.png",width = 800, height = 600) - print(pD) - dev.off() - - ggsave("DPlot.pdf", pD, width=10, height=7) -} - -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") + scale_fill_manual(values=sample.colors) -pJ = pJ + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) -write.table(x=PRODFJ, file="JFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) - -png("JPlot.png",width = 800, height = 600) -pJ -dev.off() - -ggsave("JPlot.pdf", pJ) - -# ---------------------- Now the frequency plots of the V, D and J families ---------------------- - -print("Report Clonality - V, D and J family plots") - -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$total -VPlot = ggplot(VGenes) -VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - ggtitle("Distribution of V gene families") + - ylab("Percentage of sequences") + - scale_fill_manual(values=sample.colors) + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) -png("VFPlot.png") -VPlot -dev.off() -ggsave("VFPlot.pdf", VPlot) - -write.table(x=VGenes, file="VFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) - -if(useD){ - DGenes = PRODF[,c("Sample", "Top.D.Gene")] - DGenes$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$total - DPlot = ggplot(DGenes) - DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - ggtitle("Distribution of D gene families") + - ylab("Percentage of sequences") + - scale_fill_manual(values=sample.colors) + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) - png("DFPlot.png") - print(DPlot) - dev.off() - - ggsave("DFPlot.pdf", DPlot) - write.table(x=DGenes, file="DFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) -} - -# ---------------------- Plotting the cdr3 length ---------------------- - -print("Report Clonality - CDR3 length plot") - -CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length")]) -TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample]) -CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample") -CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total -CDR3LengthPlot = ggplot(CDR3Length) -CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = factor(reorder(CDR3.Length, as.numeric(CDR3.Length))), y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - ggtitle("Length distribution of CDR3") + - xlab("CDR3 Length") + - ylab("Percentage of sequences") + - scale_fill_manual(values=sample.colors) + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) -png("CDR3LengthPlot.png",width = 1280, height = 720) -CDR3LengthPlot -dev.off() - -ggsave("CDR3LengthPlot.pdf", CDR3LengthPlot, width=12, height=7) - -write.table(x=CDR3Length, file="CDR3LengthPlot.txt", sep="\t",quote=F,row.names=F,col.names=T) - -# ---------------------- Plot the heatmaps ---------------------- - -#get the reverse order for the V and D genes -revVchain = Vchain -revDchain = Dchain -revVchain$chr.orderV = rev(revVchain$chr.orderV) -revDchain$chr.orderD = rev(revDchain$chr.orderD) - -if(useD){ - print("Report Clonality - Heatmaps VD") - plotVD <- function(dat){ - if(length(dat[,1]) == 0){ - return() - } - - img = ggplot() + - geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + - theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - scale_fill_gradient(low="gold", high="blue", na.value="white") + - ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + - xlab("D genes") + - ylab("V Genes") + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) - - png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name))) - print(img) - dev.off() - - ggsave(paste("HeatmapVD_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=13, width=8) - - write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA) - } - - VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")]) - - VandDCount$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 - check = is.nan(VandDCount$relLength) - if(any(check)){ - VandDCount[check,"relLength"] = 0 - } - - completeVD = merge(VandDCount, revVchain, by.x="Top.V.Gene", by.y="v.name", all=TRUE) - completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all=TRUE) - - fltr = is.nan(completeVD$relLength) - if(all(fltr)){ - completeVD[fltr,"relLength"] = 0 - } - - VDList = split(completeVD, f=completeVD[,"Sample"]) - lapply(VDList, FUN=plotVD) -} - -print("Report Clonality - Heatmaps VJ") - -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") + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) - - png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name))) - print(img) - dev.off() - - ggsave(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=11, width=4) - - write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",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$max - -check = is.nan(VandJCount$relLength) -if(any(check)){ - VandJCount[check,"relLength"] = 0 -} - -completeVJ = merge(VandJCount, revVchain, by.x="Top.V.Gene", by.y="v.name", all=TRUE) -completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all=TRUE) - -fltr = is.nan(completeVJ$relLength) -if(any(fltr)){ - completeVJ[fltr,"relLength"] = 1 -} - -VJList = split(completeVJ, f=completeVJ[,"Sample"]) -lapply(VJList, FUN=plotVJ) - - - -if(useD){ - print("Report Clonality - Heatmaps DJ") - plotDJ <- function(dat){ - if(length(dat[,1]) == 0){ - return() - } - img = ggplot() + - geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) + - theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - scale_fill_gradient(low="gold", high="blue", na.value="white") + - ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + - xlab("J genes") + - ylab("D Genes") + - theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) - - png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name))) - print(img) - dev.off() - - ggsave(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, width=4, height=7) - - write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA) - } - - - DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")]) - - DandJCount$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 - - check = is.nan(DandJCount$relLength) - if(any(check)){ - DandJCount[check,"relLength"] = 0 - } - - cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name) - - completeDJ = merge(DandJCount, revDchain, by.x="Top.D.Gene", by.y="v.name", all=TRUE) - completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all=TRUE) - - fltr = is.nan(completeDJ$relLength) - if(any(fltr)){ - completeDJ[fltr, "relLength"] = 1 - } - - DJList = split(completeDJ, f=completeDJ[,"Sample"]) - lapply(DJList, FUN=plotDJ) -} - - -# ---------------------- output tables for the circos plots ---------------------- - -print("Report Clonality - Circos data") - -for(smpl in unique(PRODF$Sample)){ - PRODF.sample = PRODF[PRODF$Sample == smpl,] - - fltr = PRODF.sample$Top.V.Gene == "" - if(any(fltr, na.rm=T)){ - PRODF.sample[fltr, "Top.V.Gene"] = "NA" - } - - fltr = PRODF.sample$Top.D.Gene == "" - if(any(fltr, na.rm=T)){ - PRODF.sample[fltr, "Top.D.Gene"] = "NA" - } - - fltr = PRODF.sample$Top.J.Gene == "" - if(any(fltr, na.rm=T)){ - PRODF.sample[fltr, "Top.J.Gene"] = "NA" - } - - v.d = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.D.Gene) - v.j = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.J.Gene) - d.j = table(PRODF.sample$Top.D.Gene, PRODF.sample$Top.J.Gene) - - write.table(v.d, file=paste(smpl, "_VD_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) - write.table(v.j, file=paste(smpl, "_VJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) - write.table(d.j, file=paste(smpl, "_DJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) -} - -# ---------------------- calculating the clonality score ---------------------- - -if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available -{ - print("Report Clonality - Clonality") - write.table(clonalityFrame, "clonalityComplete.txt", sep="\t",quote=F,row.names=F,col.names=T) - if(clonality_method == "boyd"){ - samples = split(clonalityFrame, clonalityFrame$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 - - write.table(res, file=paste("raw_clonality_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F) - write.table(as.matrix(res[,2:ncol(res)]), file=paste("raw_clonality2_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F) - - res = read.table(paste("raw_clonality_", sample_id, ".txt", sep=""), header=F, sep="\t", quote="", stringsAsFactors=F, fill=T, comment.char="") - - infer.result = infer.clonality(as.matrix(res[,2:ncol(res)])) - - #print(infer.result) - - write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".txt", sep=""), sep="\t",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, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - } - } - clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")]) - - #write files for every coincidence group of >1 - samples = unique(clonalFreq$Sample) - for(sample in samples){ - clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,] - if(max(clonalFreqSample$Type) > 1){ - for(i in 2:max(clonalFreqSample$Type)){ - clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,] - clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,] - clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),] - write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) - } - } - } - - 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$Reads = as.numeric(ReplicateReads$Reads) - ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads) - - ReplicatePrint <- function(dat){ - write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSplit, FUN=ReplicatePrint) - - ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")]) - clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) - - ReplicateSumPrint <- function(dat){ - write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) - - clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")]) - clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T) - clonalFreqCount$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$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) - } - - clonalityScore = clonalFreqCount[c("Sample", "Result")] - clonalityScore = unique(clonalityScore) - - clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"]) - lapply(clonalityScoreSplit, FUN=ClonalityScorePrint) - - clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")] - - - - ClonalityOverviewPrint <- function(dat){ - dat = dat[order(dat[,2]),] - write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) - } - - clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample) - lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint) - -} - -bak = PRODF -bakun = UNPROD - -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") - - #ensure certain columns are in the data (files generated with older versions of IMGT Loader) - col.checks = c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb") - for(col.check in col.checks){ - if(!(col.check %in% names(PRODF))){ - print(paste(col.check, "not found adding new column")) - if(nrow(PRODF) > 0){ #because R is anoying... - PRODF[,col.check] = 0 - } else { - PRODF = cbind(PRODF, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0))) - } - if(nrow(UNPROD) > 0){ - UNPROD[,col.check] = 0 - } else { - UNPROD = cbind(UNPROD, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0))) - } - } - } - - PRODF.with.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) > 2,] - PRODF.no.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) < 4,] - write.table(PRODF.no.D, "productive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) - - UNPROD.with.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) > 2,] - UNPROD.no.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) < 4,] - write.table(UNPROD.no.D, "unproductive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) - - num_median = function(x, na.rm=T) { as.numeric(median(x, na.rm=na.rm)) } - - newData = data.frame(data.table(PRODF.with.D)[,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(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - newData = data.frame(data.table(PRODF.with.D)[,list(unique=.N, - VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), - P1=num_median(.SD$P3V.nt.nb, na.rm=T), - N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)), - P2=num_median(.SD$P5D.nt.nb, na.rm=T), - DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), - DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), - P3=num_median(.SD$P3D.nt.nb, na.rm=T), - N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - P4=num_median(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - newData = data.frame(data.table(UNPROD.with.D)[,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(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisUnProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - newData = data.frame(data.table(UNPROD.with.D)[,list(unique=.N, - VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), - P1=num_median(.SD$P3V.nt.nb, na.rm=T), - N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)), - P2=num_median(.SD$P5D.nt.nb, na.rm=T), - DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), - DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), - P3=num_median(.SD$P3D.nt.nb, na.rm=T), - N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - P4=num_median(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), - Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisUnProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - #---------------- again for no-D - - newData = data.frame(data.table(PRODF.no.D)[,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$N.REGION.nt.nb, na.rm=T), - P2=mean(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T), - Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N, - VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), - P1=num_median(.SD$P3V.nt.nb, na.rm=T), - N1=num_median(.SD$N.REGION.nt.nb, na.rm=T), - P2=num_median(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T), - Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - newData = data.frame(data.table(UNPROD.no.D)[,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$N.REGION.nt.nb, na.rm=T), - P2=mean(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T), - Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisUnProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - - - newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N, - VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), - P1=num_median(.SD$P3V.nt.nb, na.rm=T), - N1=num_median(.SD$N.REGION.nt.nb, na.rm=T), - P2=num_median(.SD$P5J.nt.nb, na.rm=T), - DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), - Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), - Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T), - Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), - Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), - by=c("Sample")]) - newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) - write.table(newData, "junctionAnalysisUnProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) -} - -PRODF = bak -UNPROD = bakun - - -# ---------------------- D reading frame ---------------------- - -D.REGION.reading.frame = PRODF[,c("Sample", "D.REGION.reading.frame")] - -chck = is.na(D.REGION.reading.frame$D.REGION.reading.frame) -if(any(chck)){ - D.REGION.reading.frame[chck,"D.REGION.reading.frame"] = "No D" -} - -D.REGION.reading.frame.1 = data.frame(data.table(D.REGION.reading.frame)[, list(Freq=.N), by=c("Sample", "D.REGION.reading.frame")]) - -D.REGION.reading.frame.2 = data.frame(data.table(D.REGION.reading.frame)[, list(sample.sum=sum(as.numeric(.SD$D.REGION.reading.frame), na.rm=T)), by=c("Sample")]) - -D.REGION.reading.frame = merge(D.REGION.reading.frame.1, D.REGION.reading.frame.2, by="Sample") - -D.REGION.reading.frame$percentage = round(D.REGION.reading.frame$Freq / D.REGION.reading.frame$sample.sum * 100, 1) - -write.table(D.REGION.reading.frame, "DReadingFrame.txt" , sep="\t",quote=F,row.names=F,col.names=T) - -D.REGION.reading.frame = ggplot(D.REGION.reading.frame) -D.REGION.reading.frame = D.REGION.reading.frame + geom_bar(aes( x = D.REGION.reading.frame, y = percentage, fill=Sample), stat='identity', position='dodge' ) + ggtitle("D reading frame") + xlab("Frame") + ylab("Frequency") -D.REGION.reading.frame = D.REGION.reading.frame + scale_fill_manual(values=sample.colors) -D.REGION.reading.frame = D.REGION.reading.frame + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) - -png("DReadingFrame.png") -D.REGION.reading.frame -dev.off() - -ggsave("DReadingFrame.pdf", D.REGION.reading.frame) - -# ---------------------- AA composition in CDR3 ---------------------- - -AACDR3 = PRODF[,c("Sample", "CDR3.Seq")] - -TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$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$order.aa),] - -AAfreqplot = ggplot(AAfreq) -AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' ) -AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) -AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) -AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) -AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) -AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage") + scale_fill_manual(values=sample.colors) -AAfreqplot = AAfreqplot + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) - -png("AAComposition.png",width = 1280, height = 720) -AAfreqplot -dev.off() - -ggsave("AAComposition.pdf", AAfreqplot, width=12, height=7) - -write.table(AAfreq, "AAComposition.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) - -# ---------------------- AA median CDR3 length ---------------------- - -median.aa.l = data.frame(data.table(PRODF)[, list(median=as.double(median(as.numeric(.SD$CDR3.Length, na.rm=T), na.rm=T))), by=c("Sample")]) -write.table(median.aa.l, "AAMedianBySample.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) - -if(clonaltype != "none"){ - #generate the "Sequences that are present in more than one replicate" dataset - clonaltype.in.replicates = inputdata - clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$Functionality %in% c("productive (see comment)","productive"),] - clonaltype.in.replicates = clonaltype.in.replicates[!(is.na(clonaltype.in.replicates$ID) | is.na(clonaltype.in.replicates$Top.V.Gene) | is.na(clonaltype.in.replicates$Top.J.Gene)),] - clonaltype = unlist(strsplit(clonaltype, ",")) - - clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":")) - - clonaltype.in.replicates = clonaltype.in.replicates[!duplicated(clonaltype.in.replicates$clonaltype),] - - clonaltype = clonaltype[-which(clonaltype == "Sample")] - - clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":")) - clonaltype.in.replicates = clonaltype.in.replicates[,c("clonaltype","Replicate", "ID", "Sequence", "Sample")] - - - write.table(clonaltype.in.replicates, "clonaltypes_replicates_before_table.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) - - clonaltype.counts = data.frame(table(clonaltype.in.replicates$clonaltype)) - - write.table(clonaltype.counts, "clonaltypes_counts.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) - - names(clonaltype.counts) = c("clonaltype", "coincidence") - - clonaltype.counts = clonaltype.counts[clonaltype.counts$coincidence > 1,] - - clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$clonaltype %in% clonaltype.counts$clonaltype,] - clonaltype.in.replicates = merge(clonaltype.in.replicates, clonaltype.counts, by="clonaltype") - clonaltype.in.replicates = clonaltype.in.replicates[order(-clonaltype.in.replicates$coincidence, clonaltype.in.replicates$clonaltype, clonaltype.in.replicates$Replicate),c("coincidence","clonaltype", "Sample", "Replicate", "ID", "Sequence")] - - - write.table(clonaltype.in.replicates, "clonaltypes_replicates.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) -} else { - cat("No clonaltype", file="clonaltypes_replicates_before_table.txt") - cat("No clonaltype", file="clonaltypes_counts.txt") - cat("No clonaltype", file="clonaltypes_replicates.txt") -} - - - - - - - - - - - - - - - - - - - - - - - +# ---------------------- 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 ---------------------- + +print("Report Clonality - Data preperation") + +inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="", stringsAsFactors=F) + +inputdata$Sample = as.character(inputdata$Sample) + + +print(paste("nrows: ", nrow(inputdata))) + +setwd(outdir) + +# remove weird rows +inputdata = inputdata[inputdata$Sample != "",] + +print(paste("nrows: ", nrow(inputdata))) + +#remove the allele from the V,D and J genes +inputdata$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$Top.J.Gene) + +print(paste("nrows: ", nrow(inputdata))) + +#filter uniques +inputdata.removed = inputdata[NULL,] + +print(paste("nrows: ", nrow(inputdata))) + +inputdata$clonaltype = 1:nrow(inputdata) + +#keep track of the count of sequences in samples or samples/replicates for the front page overview +input.sample.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample")]) +input.rep.count = data.frame(data.table(inputdata)[, list(All=.N), by=c("Sample", "Replicate")]) + +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)", ] + PRODF = inputdata[inputdata$Functionality %in% c("productive (see comment)","productive"),] + + PRODF.count = data.frame(data.table(PRODF)[, list(count=.N), by=c("Sample")]) + + UNPROD = inputdata[inputdata$Functionality %in% c("unproductive (see comment)","unproductive"), ] + } 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" ), ] + } +} + +for(i in 1:nrow(UNPROD)){ + if(!is.numeric(UNPROD[i,"CDR3.Length"])){ + UNPROD[i,"CDR3.Length"] = 0 + } +} + +prod.sample.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample")]) +prod.rep.count = data.frame(data.table(PRODF)[, list(Productive=.N), by=c("Sample", "Replicate")]) + +unprod.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample")]) +unprod.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive=.N), by=c("Sample", "Replicate")]) + +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$clonality_clonaltype), ] +} + +if(nrow(PRODF) == 0){ + stop("No sequences left after filtering") +} + +prod.unique.sample.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample")]) +prod.unique.rep.count = data.frame(data.table(PRODF)[, list(Productive_unique=.N), by=c("Sample", "Replicate")]) + +unprod.unique.sample.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample")]) +unprod.unique.rep.count = data.frame(data.table(UNPROD)[, list(Unproductive_unique=.N), by=c("Sample", "Replicate")]) + + +PRODF$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$freq = 1 + } +} + +#make a names list with sample -> color +naive.colors = c('blue4', 'darkred', 'olivedrab3', 'red', 'gray74', 'darkviolet', 'lightblue1', 'gold', 'chartreuse2', 'pink', 'Paleturquoise3', 'Chocolate1', 'Yellow', 'Deeppink3', 'Mediumorchid1', 'Darkgreen', 'Blue', 'Gray36', 'Hotpink', 'Yellow4') +unique.samples = unique(PRODF$Sample) + +if(length(unique.samples) <= length(naive.colors)){ + sample.colors = naive.colors[1:length(unique.samples)] +} else { + sample.colors = rainbow(length(unique.samples)) +} + +names(sample.colors) = unique.samples + +print("Sample.colors") +print(sample.colors) + + +#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="\t",quote=F,row.names=F,col.names=T) +#write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T) +write.table(UNPROD, "allUnproductive.txt", sep="\t",quote=F,row.names=F,col.names=T) + +print("SAMPLE TABLE:") +print(table(PRODF$Sample)) + +#write the samples to a file +sampleFile <- file("samples.txt") +un = unique(inputdata$Sample) +un = paste(un, sep="\n") +writeLines(un, sampleFile) +close(sampleFile) + +# ---------------------- Counting the productive/unproductive and unique sequences ---------------------- + +print("Report Clonality - counting productive/unproductive/unique") + +#create the table on the overview page with the productive/unique counts per sample/replicate +#first for sample + +sample.count = merge(input.sample.count, prod.sample.count, by="Sample", all.x=T) +sample.count$perc_prod = round(sample.count$Productive / sample.count$All * 100) +sample.count = merge(sample.count, prod.unique.sample.count, by="Sample", all.x=T) +sample.count$perc_prod_un = round(sample.count$Productive_unique / sample.count$All * 100) + +sample.count = merge(sample.count , unprod.sample.count, by="Sample", all.x=T) +sample.count$perc_unprod = round(sample.count$Unproductive / sample.count$All * 100) +sample.count = merge(sample.count, unprod.unique.sample.count, by="Sample", all.x=T) +sample.count$perc_unprod_un = round(sample.count$Unproductive_unique / sample.count$All * 100) + +#then sample/replicate +rep.count = merge(input.rep.count, prod.rep.count, by=c("Sample", "Replicate"), all.x=T) + +print(rep.count) + +fltr = is.na(rep.count$Productive) +if(any(fltr)){ + rep.count[fltr,"Productive"] = 0 +} + +print(rep.count) + +rep.count$perc_prod = round(rep.count$Productive / rep.count$All * 100) +rep.count = merge(rep.count, prod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T) +rep.count$perc_prod_un = round(rep.count$Productive_unique / rep.count$All * 100) + +rep.count = merge(rep.count, unprod.rep.count, by=c("Sample", "Replicate"), all.x=T) +rep.count$perc_unprod = round(rep.count$Unproductive / rep.count$All * 100) +rep.count = merge(rep.count, unprod.unique.rep.count, by=c("Sample", "Replicate"), all.x=T) +rep.count$perc_unprod_un = round(rep.count$Unproductive_unique / rep.count$All * 100) + +rep.count$Sample = paste(rep.count$Sample, rep.count$Replicate, sep="_") +rep.count = rep.count[,names(rep.count) != "Replicate"] + +count = rbind(sample.count, rep.count) + + + +write.table(x=count, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F) + +# ---------------------- V+J+CDR3 sequence count ---------------------- + +VJCDR3.count = data.frame(table(clonalityFrame$Top.V.Gene, clonalityFrame$Top.J.Gene, clonalityFrame$CDR3.Seq.DNA)) +names(VJCDR3.count) = c("Top.V.Gene", "Top.J.Gene", "CDR3.Seq.DNA", "Count") + +VJCDR3.count = VJCDR3.count[VJCDR3.count$Count > 0,] +VJCDR3.count = VJCDR3.count[order(-VJCDR3.count$Count),] + +write.table(x=VJCDR3.count, file="VJCDR3_count.txt", sep="\t",quote=F,row.names=F,col.names=T) + +# ---------------------- Frequency calculation for V, D and J ---------------------- + +print("Report Clonality - frequency calculation V, D and J") + +PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")]) +Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$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 ---------------------- + +print("Report Clonality - getting genes for 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(nrow(Vchain), "genes in V")) +print(paste(nrow(Dchain), "genes in D")) +print(paste(nrow(Jchain), "genes in J")) + +# ---------------------- 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 ---------------------- + +print("Report Clonality - V, D and J frequency plots") + +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") + scale_fill_manual(values=sample.colors) +pV = pV + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) +write.table(x=PRODFV, file="VFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) + +png("VPlot.png",width = 1280, height = 720) +pV +dev.off() + +ggsave("VPlot.pdf", pV, width=13, height=7) + +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") + scale_fill_manual(values=sample.colors) + pD = pD + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) + write.table(x=PRODFD, file="DFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) + + png("DPlot.png",width = 800, height = 600) + print(pD) + dev.off() + + ggsave("DPlot.pdf", pD, width=10, height=7) +} + +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") + scale_fill_manual(values=sample.colors) +pJ = pJ + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) +write.table(x=PRODFJ, file="JFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) + +png("JPlot.png",width = 800, height = 600) +pJ +dev.off() + +ggsave("JPlot.pdf", pJ) + +# ---------------------- Now the frequency plots of the V, D and J families ---------------------- + +print("Report Clonality - V, D and J family plots") + +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$total +VPlot = ggplot(VGenes) +VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Distribution of V gene families") + + ylab("Percentage of sequences") + + scale_fill_manual(values=sample.colors) + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) +png("VFPlot.png") +VPlot +dev.off() +ggsave("VFPlot.pdf", VPlot) + +write.table(x=VGenes, file="VFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) + +if(useD){ + DGenes = PRODF[,c("Sample", "Top.D.Gene")] + DGenes$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$total + DPlot = ggplot(DGenes) + DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Distribution of D gene families") + + ylab("Percentage of sequences") + + scale_fill_manual(values=sample.colors) + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) + png("DFPlot.png") + print(DPlot) + dev.off() + + ggsave("DFPlot.pdf", DPlot) + write.table(x=DGenes, file="DFFrequency.txt", sep="\t",quote=F,row.names=F,col.names=T) +} + +# ---------------------- Plotting the cdr3 length ---------------------- + +print("Report Clonality - CDR3 length plot") + +CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length")]) +TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample]) +CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample") +CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total +CDR3LengthPlot = ggplot(CDR3Length) +CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = factor(reorder(CDR3.Length, as.numeric(CDR3.Length))), y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + ggtitle("Length distribution of CDR3") + + xlab("CDR3 Length") + + ylab("Percentage of sequences") + + scale_fill_manual(values=sample.colors) + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) +png("CDR3LengthPlot.png",width = 1280, height = 720) +CDR3LengthPlot +dev.off() + +ggsave("CDR3LengthPlot.pdf", CDR3LengthPlot, width=12, height=7) + +write.table(x=CDR3Length, file="CDR3LengthPlot.txt", sep="\t",quote=F,row.names=F,col.names=T) + +# ---------------------- Plot the heatmaps ---------------------- + +#get the reverse order for the V and D genes +revVchain = Vchain +revDchain = Dchain +revVchain$chr.orderV = rev(revVchain$chr.orderV) +revDchain$chr.orderD = rev(revDchain$chr.orderD) + +if(useD){ + print("Report Clonality - Heatmaps VD") + plotVD <- function(dat){ + if(length(dat[,1]) == 0){ + return() + } + + img = ggplot() + + geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_gradient(low="gold", high="blue", na.value="white") + + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + + xlab("D genes") + + ylab("V Genes") + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) + + png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name))) + print(img) + dev.off() + + ggsave(paste("HeatmapVD_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=13, width=8) + + write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA) + } + + VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")]) + + VandDCount$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 + check = is.nan(VandDCount$relLength) + if(any(check)){ + VandDCount[check,"relLength"] = 0 + } + + completeVD = merge(VandDCount, revVchain, by.x="Top.V.Gene", by.y="v.name", all=TRUE) + completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all=TRUE) + + fltr = is.nan(completeVD$relLength) + if(all(fltr)){ + completeVD[fltr,"relLength"] = 0 + } + + VDList = split(completeVD, f=completeVD[,"Sample"]) + lapply(VDList, FUN=plotVD) +} + +print("Report Clonality - Heatmaps VJ") + +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") + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) + + png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name))) + print(img) + dev.off() + + ggsave(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, height=11, width=4) + + write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",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$max + +check = is.nan(VandJCount$relLength) +if(any(check)){ + VandJCount[check,"relLength"] = 0 +} + +completeVJ = merge(VandJCount, revVchain, by.x="Top.V.Gene", by.y="v.name", all=TRUE) +completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all=TRUE) + +fltr = is.nan(completeVJ$relLength) +if(any(fltr)){ + completeVJ[fltr,"relLength"] = 1 +} + +VJList = split(completeVJ, f=completeVJ[,"Sample"]) +lapply(VJList, FUN=plotVJ) + + + +if(useD){ + print("Report Clonality - Heatmaps DJ") + plotDJ <- function(dat){ + if(length(dat[,1]) == 0){ + return() + } + img = ggplot() + + geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_gradient(low="gold", high="blue", na.value="white") + + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + + xlab("J genes") + + ylab("D Genes") + + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) + + png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=200+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name))) + print(img) + dev.off() + + ggsave(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".pdf", sep=""), img, width=4, height=7) + + write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".txt", sep=""), sep="\t",quote=F,row.names=T,col.names=NA) + } + + + DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")]) + + DandJCount$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 + + check = is.nan(DandJCount$relLength) + if(any(check)){ + DandJCount[check,"relLength"] = 0 + } + + cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name) + + completeDJ = merge(DandJCount, revDchain, by.x="Top.D.Gene", by.y="v.name", all=TRUE) + completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all=TRUE) + + fltr = is.nan(completeDJ$relLength) + if(any(fltr)){ + completeDJ[fltr, "relLength"] = 1 + } + + DJList = split(completeDJ, f=completeDJ[,"Sample"]) + lapply(DJList, FUN=plotDJ) +} + + +# ---------------------- output tables for the circos plots ---------------------- + +print("Report Clonality - Circos data") + +for(smpl in unique(PRODF$Sample)){ + PRODF.sample = PRODF[PRODF$Sample == smpl,] + + fltr = PRODF.sample$Top.V.Gene == "" + if(any(fltr, na.rm=T)){ + PRODF.sample[fltr, "Top.V.Gene"] = "NA" + } + + fltr = PRODF.sample$Top.D.Gene == "" + if(any(fltr, na.rm=T)){ + PRODF.sample[fltr, "Top.D.Gene"] = "NA" + } + + fltr = PRODF.sample$Top.J.Gene == "" + if(any(fltr, na.rm=T)){ + PRODF.sample[fltr, "Top.J.Gene"] = "NA" + } + + v.d = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.D.Gene) + v.j = table(PRODF.sample$Top.V.Gene, PRODF.sample$Top.J.Gene) + d.j = table(PRODF.sample$Top.D.Gene, PRODF.sample$Top.J.Gene) + + write.table(v.d, file=paste(smpl, "_VD_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) + write.table(v.j, file=paste(smpl, "_VJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) + write.table(d.j, file=paste(smpl, "_DJ_circos.txt", sep=""), sep="\t", quote=F, row.names=T, col.names=NA) +} + +# ---------------------- calculating the clonality score ---------------------- + +if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available +{ + print("Report Clonality - Clonality") + write.table(clonalityFrame, "clonalityComplete.txt", sep="\t",quote=F,row.names=F,col.names=T) + if(clonality_method == "boyd"){ + samples = split(clonalityFrame, clonalityFrame$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 + + write.table(res, file=paste("raw_clonality_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F) + write.table(as.matrix(res[,2:ncol(res)]), file=paste("raw_clonality2_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=F) + + res = read.table(paste("raw_clonality_", sample_id, ".txt", sep=""), header=F, sep="\t", quote="", stringsAsFactors=F, fill=T, comment.char="") + + infer.result = infer.clonality(as.matrix(res[,2:ncol(res)])) + + #print(infer.result) + + write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".txt", sep=""), sep="\t",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, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + } + } + clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")]) + + #write files for every coincidence group of >1 + samples = unique(clonalFreq$Sample) + for(sample in samples){ + clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,] + if(max(clonalFreqSample$Type) > 1){ + for(i in 2:max(clonalFreqSample$Type)){ + clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,] + clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,] + clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),] + write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) + } + } + } + + 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$Reads = as.numeric(ReplicateReads$Reads) + ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads) + + ReplicatePrint <- function(dat){ + write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) + } + + ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) + lapply(ReplicateSplit, FUN=ReplicatePrint) + + ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")]) + clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) + + ReplicateSumPrint <- function(dat){ + write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) + } + + ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) + lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) + + clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")]) + clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T) + clonalFreqCount$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$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) + } + + clonalityScore = clonalFreqCount[c("Sample", "Result")] + clonalityScore = unique(clonalityScore) + + clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"]) + lapply(clonalityScoreSplit, FUN=ClonalityScorePrint) + + clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")] + + + + ClonalityOverviewPrint <- function(dat){ + dat = dat[order(dat[,2]),] + write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F) + } + + clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample) + lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint) + +} + +bak = PRODF +bakun = UNPROD + +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") + + #ensure certain columns are in the data (files generated with older versions of IMGT Loader) + col.checks = c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb") + for(col.check in col.checks){ + if(!(col.check %in% names(PRODF))){ + print(paste(col.check, "not found adding new column")) + if(nrow(PRODF) > 0){ #because R is anoying... + PRODF[,col.check] = 0 + } else { + PRODF = cbind(PRODF, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0))) + } + if(nrow(UNPROD) > 0){ + UNPROD[,col.check] = 0 + } else { + UNPROD = cbind(UNPROD, data.frame(N3.REGION.nt.nb=numeric(0), N4.REGION.nt.nb=numeric(0))) + } + } + } + + PRODF.with.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) > 2,] + PRODF.no.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) < 4,] + write.table(PRODF.no.D, "productive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) + + UNPROD.with.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) > 2,] + UNPROD.no.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) < 4,] + write.table(UNPROD.no.D, "unproductive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) + + num_median = function(x, na.rm=T) { as.numeric(median(x, na.rm=na.rm)) } + + newData = data.frame(data.table(PRODF.with.D)[,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(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(PRODF.with.D)[,list(unique=.N, + VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=num_median(.SD$P3V.nt.nb, na.rm=T), + N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)), + P2=num_median(.SD$P5D.nt.nb, na.rm=T), + DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), + DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), + P3=num_median(.SD$P3D.nt.nb, na.rm=T), + N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + P4=num_median(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(UNPROD.with.D)[,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(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], 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(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisUnProd_mean_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(UNPROD.with.D)[,list(unique=.N, + VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=num_median(.SD$P3V.nt.nb, na.rm=T), + N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb"), with=F], na.rm=T)), + P2=num_median(.SD$P5D.nt.nb, na.rm=T), + DEL.DH=num_median(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T), + DH.DEL=num_median(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T), + P3=num_median(.SD$P3D.nt.nb, na.rm=T), + N2=num_median(rowSums(.SD[,c("N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + P4=num_median(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "N3.REGION.nt.nb", "N4.REGION.nt.nb"), with=F], na.rm=T)), + Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisUnProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + #---------------- again for no-D + + newData = data.frame(data.table(PRODF.no.D)[,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$N.REGION.nt.nb, na.rm=T), + P2=mean(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T), + Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N, + VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=num_median(.SD$P3V.nt.nb, na.rm=T), + N1=num_median(.SD$N.REGION.nt.nb, na.rm=T), + P2=num_median(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T), + Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + newData = data.frame(data.table(UNPROD.no.D)[,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$N.REGION.nt.nb, na.rm=T), + P2=mean(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T), + Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisUnProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + + + newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N, + VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T), + P1=num_median(.SD$P3V.nt.nb, na.rm=T), + N1=num_median(.SD$N.REGION.nt.nb, na.rm=T), + P2=num_median(.SD$P5J.nt.nb, na.rm=T), + DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T), + Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)), + Total.N=num_median(.SD$N.REGION.nt.nb, na.rm=T), + Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)), + Median.CDR3.l=as.double(median(as.numeric(.SD$CDR3.Length), na.rm=T))), + by=c("Sample")]) + newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1) + write.table(newData, "junctionAnalysisUnProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) +} + +PRODF = bak +UNPROD = bakun + + +# ---------------------- D reading frame ---------------------- + +D.REGION.reading.frame = PRODF[,c("Sample", "D.REGION.reading.frame")] + +chck = is.na(D.REGION.reading.frame$D.REGION.reading.frame) +if(any(chck)){ + D.REGION.reading.frame[chck,"D.REGION.reading.frame"] = "No D" +} + +D.REGION.reading.frame.1 = data.frame(data.table(D.REGION.reading.frame)[, list(Freq=.N), by=c("Sample", "D.REGION.reading.frame")]) + +D.REGION.reading.frame.2 = data.frame(data.table(D.REGION.reading.frame)[, list(sample.sum=sum(as.numeric(.SD$D.REGION.reading.frame), na.rm=T)), by=c("Sample")]) + +D.REGION.reading.frame = merge(D.REGION.reading.frame.1, D.REGION.reading.frame.2, by="Sample") + +D.REGION.reading.frame$percentage = round(D.REGION.reading.frame$Freq / D.REGION.reading.frame$sample.sum * 100, 1) + +write.table(D.REGION.reading.frame, "DReadingFrame.txt" , sep="\t",quote=F,row.names=F,col.names=T) + +D.REGION.reading.frame = ggplot(D.REGION.reading.frame) +D.REGION.reading.frame = D.REGION.reading.frame + geom_bar(aes( x = D.REGION.reading.frame, y = percentage, fill=Sample), stat='identity', position='dodge' ) + ggtitle("D reading frame") + xlab("Frame") + ylab("Frequency") +D.REGION.reading.frame = D.REGION.reading.frame + scale_fill_manual(values=sample.colors) +D.REGION.reading.frame = D.REGION.reading.frame + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) + +png("DReadingFrame.png") +D.REGION.reading.frame +dev.off() + +ggsave("DReadingFrame.pdf", D.REGION.reading.frame) + +# ---------------------- AA composition in CDR3 ---------------------- + +AACDR3 = PRODF[,c("Sample", "CDR3.Seq")] + +TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$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$order.aa),] + +AAfreqplot = ggplot(AAfreq) +AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' ) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2) +AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2) +AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage") + scale_fill_manual(values=sample.colors) +AAfreqplot = AAfreqplot + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major.y = element_line(colour = "black"), panel.grid.major.x = element_blank()) + +png("AAComposition.png",width = 1280, height = 720) +AAfreqplot +dev.off() + +ggsave("AAComposition.pdf", AAfreqplot, width=12, height=7) + +write.table(AAfreq, "AAComposition.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) + +# ---------------------- AA median CDR3 length ---------------------- + +median.aa.l = data.frame(data.table(PRODF)[, list(median=as.double(median(as.numeric(.SD$CDR3.Length, na.rm=T), na.rm=T))), by=c("Sample")]) +write.table(median.aa.l, "AAMedianBySample.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F) + +if(clonaltype != "none"){ + #generate the "Sequences that are present in more than one replicate" dataset + clonaltype.in.replicates = inputdata + clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$Functionality %in% c("productive (see comment)","productive"),] + clonaltype.in.replicates = clonaltype.in.replicates[!(is.na(clonaltype.in.replicates$ID) | is.na(clonaltype.in.replicates$Top.V.Gene) | is.na(clonaltype.in.replicates$Top.J.Gene)),] + clonaltype = unlist(strsplit(clonaltype, ",")) + + clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":")) + + clonaltype.in.replicates = clonaltype.in.replicates[!duplicated(clonaltype.in.replicates$clonaltype),] + + clonaltype = clonaltype[-which(clonaltype == "Sample")] + + clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":")) + clonaltype.in.replicates = clonaltype.in.replicates[,c("clonaltype","Replicate", "ID", "Sequence", "Sample")] + + + write.table(clonaltype.in.replicates, "clonaltypes_replicates_before_table.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) + + clonaltype.counts = data.frame(table(clonaltype.in.replicates$clonaltype)) + + write.table(clonaltype.counts, "clonaltypes_counts.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) + + names(clonaltype.counts) = c("clonaltype", "coincidence") + + clonaltype.counts = clonaltype.counts[clonaltype.counts$coincidence > 1,] + + clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$clonaltype %in% clonaltype.counts$clonaltype,] + clonaltype.in.replicates = merge(clonaltype.in.replicates, clonaltype.counts, by="clonaltype") + clonaltype.in.replicates = clonaltype.in.replicates[order(-clonaltype.in.replicates$coincidence, clonaltype.in.replicates$clonaltype, clonaltype.in.replicates$Replicate),c("coincidence","clonaltype", "Sample", "Replicate", "ID", "Sequence")] + + + write.table(clonaltype.in.replicates, "clonaltypes_replicates.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T) +} else { + cat("No clonaltype", file="clonaltypes_replicates_before_table.txt") + cat("No clonaltype", file="clonaltypes_counts.txt") + cat("No clonaltype", file="clonaltypes_replicates.txt") +} + + + + + + + + + + + + + + + + + + + + + + +