Mercurial > repos > davidvanzessen > argalaxy_tools
view report_clonality/RScript.r @ 42:9a47d7a552d6 draft
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| author | davidvanzessen |
|---|---|
| date | Thu, 26 Oct 2017 09:08:37 -0400 |
| parents | a70dbcfa5d8a |
| children | 2325074a8461 |
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# ---------------------- 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$Length, na.rm=T) ,")", sep="")) + xlab("D genes") + ylab("V Genes") + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$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$relLength) if(any(check)){ VandDCount[check,"relLength"] = 0 } cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name) completeVD = merge(VandDCount, cartegianProductVD, by.x=c("Top.V.Gene", "Top.D.Gene"), by.y=c("Top.V.Gene", "Top.D.Gene"), all=TRUE) completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) fltr = is.nan(completeVD$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=150+(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 } cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name) completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE) completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) 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$Length, na.rm=T) ,")", sep="")) + xlab("J genes") + ylab("D Genes") + theme(panel.background = element_rect(fill = "white", colour="black"),text = element_text(size=15, colour="black"), panel.grid.major = element_line(colour = "gainsboro")) png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$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$v.name) completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE) completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) fltr = is.nan(completeDJ$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) #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$clonaltype = do.call(paste, c(clonaltype.in.replicates[c(clonaltype, "Replicate")], sep = ":")) clonaltype.in.replicates = clonaltype.in.replicates[!duplicated(clonaltype.in.replicates$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)