Mercurial > repos > davidvanzessen > complete_immunerepertoire_igg
view RScript.r @ 11:1b356cbde361 draft
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| author | davidvanzessen |
|---|---|
| date | Tue, 23 Jun 2015 07:52:29 -0400 |
| parents | b8db36cfe6ad |
| children |
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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) # ---------------------- 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) # ---------------------- Data preperation ---------------------- inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="") setwd(outdir) # remove weird rows inputdata = inputdata[inputdata$Sample != "",] #remove the allele from the V,D and J genes inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene) inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene) inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene) inputdata$clonaltype = 1:nrow(inputdata) PRODF = inputdata UNPROD = inputdata if(filterproductive){ if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ] UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ] } else { PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ] UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ] } } #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), ] } PRODF$freq = 1 if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*" PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID) PRODF$freq = gsub("_.*", "", PRODF$freq) PRODF$freq = as.numeric(PRODF$freq) if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence PRODF$freq = 1 } } #write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T) #write the samples to a file sampleFile <- file("samples.txt") un = unique(inputdata$Sample) un = paste(un, sep="\n") writeLines(un, sampleFile) close(sampleFile) # ---------------------- Counting the productive/unproductive and unique sequences ---------------------- inputdata.dt = data.table(inputdata) #for speed if(clonaltype == "none"){ ct = c("clonaltype") } inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_") samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample))) frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)]) sample_productive_count = inputdata.dt[, list(All=.N, Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), perc_prod = 1, Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), perc_prod_un = 1, Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]), perc_unprod = 1, Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]), perc_unprod_un = 1), by=c("Sample")] sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100) sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100) sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100) sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100) sample_replicate_productive_count = inputdata.dt[, list(All=.N, Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), perc_prod = 1, Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), perc_prod_un = 1, Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]), perc_unprod = 1, Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]), perc_unprod_un = 1), by=c("samples_replicates")] sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100) sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100) sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100) sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100) setnames(sample_replicate_productive_count, colnames(sample_productive_count)) counts = rbind(sample_replicate_productive_count, sample_productive_count) counts = counts[order(counts$Sample),] write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F) # ---------------------- Frequency calculation for V, D and J ---------------------- PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")]) Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length))) PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE) PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total)) PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")]) Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length))) PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE) PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total)) PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")]) Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length))) PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE) PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total)) # ---------------------- Setting up the gene names for the different T/B, human/mouse and locus ---------------------- V = c("v.name\tchr.orderV\n") D = c("v.name\tchr.orderD\n") J = c("v.name\tchr.orderJ\n") if(species == "human"){ if(locus == "trb"){ V = c("v.name\tchr.orderV\nTRBV2\t1\nTRBV3-1\t2\nTRBV4-1\t3\nTRBV5-1\t4\nTRBV6-1\t5\nTRBV4-2\t6\nTRBV6-2\t7\nTRBV4-3\t8\nTRBV6-3\t9\nTRBV7-2\t10\nTRBV6-4\t11\nTRBV7-3\t12\nTRBV9\t13\nTRBV10-1\t14\nTRBV11-1\t15\nTRBV10-2\t16\nTRBV11-2\t17\nTRBV6-5\t18\nTRBV7-4\t19\nTRBV5-4\t20\nTRBV6-6\t21\nTRBV5-5\t22\nTRBV7-6\t23\nTRBV5-6\t24\nTRBV6-8\t25\nTRBV7-7\t26\nTRBV6-9\t27\nTRBV7-8\t28\nTRBV5-8\t29\nTRBV7-9\t30\nTRBV13\t31\nTRBV10-3\t32\nTRBV11-3\t33\nTRBV12-3\t34\nTRBV12-4\t35\nTRBV12-5\t36\nTRBV14\t37\nTRBV15\t38\nTRBV16\t39\nTRBV18\t40\nTRBV19\t41\nTRBV20-1\t42\nTRBV24-1\t43\nTRBV25-1\t44\nTRBV27\t45\nTRBV28\t46\nTRBV29-1\t47\nTRBV30\t48") D = c("v.name\tchr.orderD\nTRBD1\t1\nTRBD2\t2\n") J = c("v.name\tchr.orderJ\nTRBJ1-1\t1\nTRBJ1-2\t2\nTRBJ1-3\t3\nTRBJ1-4\t4\nTRBJ1-5\t5\nTRBJ1-6\t6\nTRBJ2-1\t7\nTRBJ2-2\t8\nTRBJ2-3\t9\nTRBJ2-4\t10\nTRBJ2-5\t11\nTRBJ2-6\t12\nTRBJ2-7\t13") } else if (locus == "tra"){ V = c("v.name\tchr.orderVTRAV1-1\t1\nTRAV1-2\t2\nTRAV2\t3\nTRAV3\t4\nTRAV4\t5\nTRAV5\t6\nTRAV6\t7\nTRAV7\t8\nTRAV8-1\t9\nTRAV9-1\t10\nTRAV10\t11\nTRAV12-1\t12\nTRAV8-2\t13\nTRAV8-3\t14\nTRAV13-1\t15\nTRAV12-2\t16\nTRAV8-4\t17\nTRAV13-2\t18\nTRAV14/DV4\t19\nTRAV9-2\t20\nTRAV12-3\t21\nTRAV8-6\t22\nTRAV16\t23\nTRAV17\t24\nTRAV18\t25\nTRAV19\t26\nTRAV20\t27\nTRAV21\t28\nTRAV22\t29\nTRAV23/DV6\t30\nTRAV24\t31\nTRAV25\t32\nTRAV26-1\t33\nTRAV27\t34\nTRAV29/DV5\t35\nTRAV30\t36\nTRAV26-2\t37\nTRAV34\t38\nTRAV35\t39\nTRAV36/DV7\t40\nTRAV38-1\t41\nTRAV38-2/DV8\t42\nTRAV39\t43\nTRAV40\t44\nTRAV41\t45\n") D = c("v.name\tchr.orderD\n") J = c("v.name\tchr.orderJ\nTRAJ57\t1\nTRAJ56\t2\nTRAJ54\t3\nTRAJ53\t4\nTRAJ52\t5\nTRAJ50\t6\nTRAJ49\t7\nTRAJ48\t8\nTRAJ47\t9\nTRAJ46\t10\nTRAJ45\t11\nTRAJ44\t12\nTRAJ43\t13\nTRAJ42\t14\nTRAJ41\t15\nTRAJ40\t16\nTRAJ39\t17\nTRAJ38\t18\nTRAJ37\t19\nTRAJ36\t20\nTRAJ34\t21\nTRAJ33\t22\nTRAJ32\t23\nTRAJ31\t24\nTRAJ30\t25\nTRAJ29\t26\nTRAJ28\t27\nTRAJ27\t28\nTRAJ26\t29\nTRAJ24\t30\nTRAJ23\t31\nTRAJ22\t32\nTRAJ21\t33\nTRAJ20\t34\nTRAJ18\t35\nTRAJ17\t36\nTRAJ16\t37\nTRAJ15\t38\nTRAJ14\t39\nTRAJ13\t40\nTRAJ12\t41\nTRAJ11\t42\nTRAJ10\t43\nTRAJ9\t44\nTRAJ8\t45\nTRAJ7\t46\nTRAJ6\t47\nTRAJ5\t48\nTRAJ4\t49\nTRAJ3\t50") } else if (locus == "trg"){ V = c("v.name\tchr.orderV\nTRGV9\t1\nTRGV8\t2\nTRGV5\t3\nTRGV4\t4\nTRGV3\t5\nTRGV2\t6") D = c("v.name\tchr.orderD\n") J = c("v.name\tchr.orderJ\nTRGJ2\t1\nTRGJP2\t2\nTRGJ1\t3\nTRGJP1\t4") } else if (locus == "trd"){ V = c("v.name\tchr.orderV\nTRDV1\t1\nTRDV2\t2\nTRDV3\t3") D = c("v.name\tchr.orderD\nTRDD1\t1\nTRDD2\t2\nTRDD3\t3") J = c("v.name\tchr.orderJ\nTRDJ1\t1\nTRDJ4\t2\nTRDJ2\t3\nTRDJ3\t4") } else if(locus == "igh"){ V = c("v.name\tchr.orderV\nIGHV3-74\t1\nIGHV3-73\t2\nIGHV3-72\t3\nIGHV2-70\t4\nIGHV1-69D\t5\nIGHV1-69-2\t6\nIGHV2-70D\t7\nIGHV1-69\t8\nIGHV3-66\t9\nIGHV3-64\t10\nIGHV4-61\t11\nIGHV4-59\t12\nIGHV1-58\t13\nIGHV3-53\t14\nIGHV5-51\t15\nIGHV3-49\t16\nIGHV3-48\t17\nIGHV1-46\t18\nIGHV1-45\t19\nIGHV3-43\t20\nIGHV4-39\t21\nIGHV3-43D\t22\nIGHV4-38-2\t23\nIGHV4-34\t24\nIGHV3-33\t25\nIGHV4-31\t26\nIGHV3-30-5\t27\nIGHV4-30-4\t28\nIGHV3-30-3\t29\nIGHV4-30-2\t30\nIGHV4-30-1\t31\nIGHV3-30\t32\nIGHV4-28\t33\nIGHV2-26\t34\nIGHV1-24\t35\nIGHV3-23D\t36\nIGHV3-23\t37\nIGHV3-21\t38\nIGHV3-20\t39\nIGHV1-18\t40\nIGHV3-15\t41\nIGHV3-13\t42\nIGHV3-11\t43\nIGHV5-10-1\t44\nIGHV3-9\t45\nIGHV1-8\t46\nIGHV3-64D\t47\nIGHV3-7\t48\nIGHV2-5\t49\nIGHV7-4-1\t50\nIGHV4-4\t51\nIGHV1-3\t52\nIGHV1-2\t53\nIGHV6-1\t54") D = c("v.name\tchr.orderD\nIGHD1-7\t1\nIGHD2-8\t2\nIGHD3-9\t3\nIGHD3-10\t4\nIGHD5-12\t5\nIGHD6-13\t6\nIGHD2-15\t7\nIGHD3-16\t8\nIGHD4-17\t9\nIGHD5-18\t10\nIGHD6-19\t11\nIGHD1-20\t12\nIGHD2-21\t13\nIGHD3-22\t14\nIGHD5-24\t15\nIGHD6-25\t16\nIGHD1-26\t17\nIGHD7-27\t18") J = c("v.name\tchr.orderJ\nIGHJ1\t1\nIGHJ2\t2\nIGHJ3\t3\nIGHJ4\t4\nIGHJ5\t5\nIGHJ6\t6") } else if (locus == "igk"){ V = c("v.name\tchr.orderV\nIGKV3D-7\t1\nIGKV1D-8\t2\nIGKV1D-43\t3\nIGKV3D-11\t4\nIGKV1D-12\t5\nIGKV1D-13\t6\nIGKV3D-15\t7\nIGKV1D-16\t8\nIGKV1D-17\t9\nIGKV3D-20\t10\nIGKV2D-26\t11\nIGKV2D-28\t12\nIGKV2D-29\t13\nIGKV2D-30\t14\nIGKV1D-33\t15\nIGKV1D-39\t16\nIGKV2D-40\t17\nIGKV2-40\t18\nIGKV1-39\t19\nIGKV1-33\t20\nIGKV2-30\t21\nIGKV2-29\t22\nIGKV2-28\t23\nIGKV1-27\t24\nIGKV2-24\t25\nIGKV3-20\t26\nIGKV1-17\t27\nIGKV1-16\t28\nIGKV3-15\t29\nIGKV1-13\t30\nIGKV1-12\t31\nIGKV3-11\t32\nIGKV1-9\t33\nIGKV1-8\t34\nIGKV1-6\t35\nIGKV1-5\t36\nIGKV5-2\t37\nIGKV4-1\t38") D = c("v.name\tchr.orderD\n") J = c("v.name\tchr.orderJ\nIGKJ1\t1\nIGKJ2\t2\nIGKJ3\t3\nIGKJ4\t4\nIGKJ5\t5") } else if (locus == "igl"){ V = c("v.name\tchr.orderV\nIGLV4-69\t1\nIGLV8-61\t2\nIGLV4-60\t3\nIGLV6-57\t4\nIGLV5-52\t5\nIGLV1-51\t6\nIGLV9-49\t7\nIGLV1-47\t8\nIGLV7-46\t9\nIGLV5-45\t10\nIGLV1-44\t11\nIGLV7-43\t12\nIGLV1-41\t13\nIGLV1-40\t14\nIGLV5-39\t15\nIGLV5-37\t16\nIGLV1-36\t17\nIGLV3-27\t18\nIGLV3-25\t19\nIGLV2-23\t20\nIGLV3-22\t21\nIGLV3-21\t22\nIGLV3-19\t23\nIGLV2-18\t24\nIGLV3-16\t25\nIGLV2-14\t26\nIGLV3-12\t27\nIGLV2-11\t28\nIGLV3-10\t29\nIGLV3-9\t30\nIGLV2-8\t31\nIGLV4-3\t32\nIGLV3-1\t33") D = c("v.name\tchr.orderD\n") J = c("v.name\tchr.orderJ\nIGLJ1\t1\nIGLJ2\t2\nIGLJ3\t3\nIGLJ6\t4\nIGLJ7\t5") } } else if (species == "mouse"){ if(locus == "trb"){ V = c("v.name\tchr.orderV\nTRBV1\t1\nTRBV2\t2\nTRBV3\t3\nTRBV4\t4\nTRBV5\t5\nTRBV12-1\t6\nTRBV13-1\t7\nTRBV12-2\t8\nTRBV13-2\t9\nTRBV13-3\t10\nTRBV14\t11\nTRBV15\t12\nTRBV16\t13\nTRBV17\t14\nTRBV19\t15\nTRBV20\t16\nTRBV23\t17\nTRBV24\t18\nTRBV26\t19\nTRBV29\t20\nTRBV30\t21\nTRBV31\t22") D = c("v.name\tchr.orderD\nTRBD1\t1\nTRBD2\t2") J = c("v.name\tchr.orderJ\nTRBJ1-1\t1\nTRBJ1-2\t2\nTRBJ1-3\t3\nTRBJ1-4\t4\nTRBJ1-5\t5\nTRBJ2-1\t6\nTRBJ2-2\t7\nTRBJ2-3\t8\nTRBJ2-4\t9\nTRBJ2-5\t10\nTRBJ2-6\t11\nTRBJ2-7\t12") } else if (locus == "tra"){ cat("mouse tra not yet implemented") } else if (locus == "trg"){ cat("mouse trg not yet implemented") } else if (locus == "trd"){ cat("mouse trd not yet implemented") } else if(locus == "igh"){ cat("mouse igh not yet implemented") } else if (locus == "igk"){ cat("mouse igk not yet implemented") } else if (locus == "igl"){ cat("mouse igl not yet implemented") } } useD = TRUE if(species == "human" && locus == "tra"){ useD = FALSE cat("No D Genes in this species/locus") } # ---------------------- load the gene names into a data.frame and merge with the frequency count ---------------------- tcV = textConnection(V) Vchain = read.table(tcV, sep="\t", header=TRUE) PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE) close(tcV) tcD = textConnection(D) Dchain = read.table(tcD, sep="\t", header=TRUE) PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE) close(tcD) tcJ = textConnection(J) Jchain = read.table(tcJ, sep="\t", header=TRUE) PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE) close(tcJ) # ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ---------------------- pV = ggplot(PRODFV) pV = pV + geom_bar( aes( x=factor(reorder(Top.V.Gene, chr.orderV)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage") write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) png("VPlot.png",width = 1280, height = 720) pV dev.off(); if(useD){ pD = ggplot(PRODFD) pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage") write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) png("DPlot.png",width = 800, height = 600) print(pD) dev.off(); } pJ = ggplot(PRODFJ) pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage") write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) png("JPlot.png",width = 800, height = 600) pJ dev.off(); pJ = ggplot(PRODFJ) pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage") write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) png("JPlot.png",width = 800, height = 600) pJ dev.off(); # ---------------------- Now the frequency plots of the V, D and J families ---------------------- VGenes = PRODF[,c("Sample", "Top.V.Gene")] VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene) VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")]) TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample]) VGenes = merge(VGenes, TotalPerSample, by="Sample") VGenes$Frequency = VGenes$Count * 100 / VGenes$total VPlot = ggplot(VGenes) VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle("Distribution of V gene families") + ylab("Percentage of sequences") png("VFPlot.png") VPlot dev.off(); write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) if(useD){ DGenes = PRODF[,c("Sample", "Top.D.Gene")] DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene) DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")]) TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample]) DGenes = merge(DGenes, TotalPerSample, by="Sample") DGenes$Frequency = DGenes$Count * 100 / DGenes$total DPlot = ggplot(DGenes) DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle("Distribution of D gene families") + ylab("Percentage of sequences") png("DFPlot.png") print(DPlot) dev.off(); write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) } JGenes = PRODF[,c("Sample", "Top.J.Gene")] JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene) JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")]) TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample]) JGenes = merge(JGenes, TotalPerSample, by="Sample") JGenes$Frequency = JGenes$Count * 100 / JGenes$total JPlot = ggplot(JGenes) JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle("Distribution of J gene families") + ylab("Percentage of sequences") png("JFPlot.png") JPlot dev.off(); write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T) # ---------------------- Plotting the cdr3 length ---------------------- CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")]) TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample]) CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample") CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total CDR3LengthPlot = ggplot(CDR3Length) CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle("Length distribution of CDR3") + xlab("CDR3 Length") + ylab("Percentage of sequences") png("CDR3LengthPlot.png",width = 1280, height = 720) CDR3LengthPlot dev.off() write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T) # ---------------------- Plot the heatmaps ---------------------- #get the reverse order for the V and D genes revVchain = Vchain revDchain = Dchain revVchain$chr.orderV = rev(revVchain$chr.orderV) revDchain$chr.orderD = rev(revDchain$chr.orderD) if(useD){ plotVD <- function(dat){ if(length(dat[,1]) == 0){ return() } img = ggplot() + geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_fill_gradient(low="gold", high="blue", na.value="white") + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + xlab("D genes") + ylab("V Genes") png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name))) print(img) dev.off() write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) } VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")]) VandDCount$l = log(VandDCount$Length) maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")]) VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T) VandDCount$relLength = VandDCount$l / VandDCount$max cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample)) completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE) completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) VDList = split(completeVD, f=completeVD[,"Sample"]) lapply(VDList, FUN=plotVD) } plotVJ <- function(dat){ if(length(dat[,1]) == 0){ return() } cat(paste(unique(dat[3])[1,1])) img = ggplot() + geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_fill_gradient(low="gold", high="blue", na.value="white") + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + xlab("J genes") + ylab("V Genes") png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name))) print(img) dev.off() write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) } VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")]) VandJCount$l = log(VandJCount$Length) maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")]) VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T) VandJCount$relLength = VandJCount$l / VandJCount$max cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample)) completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE) completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE) completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) VJList = split(completeVJ, f=completeVJ[,"Sample"]) lapply(VJList, FUN=plotVJ) if(useD){ plotDJ <- function(dat){ if(length(dat[,1]) == 0){ return() } img = ggplot() + geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_fill_gradient(low="gold", high="blue", na.value="white") + ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + xlab("J genes") + ylab("D Genes") png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name))) print(img) dev.off() write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA) } DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")]) DandJCount$l = log(DandJCount$Length) maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")]) DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T) DandJCount$relLength = DandJCount$l / DandJCount$max cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample)) completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE) completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) DJList = split(completeDJ, f=completeDJ[,"Sample"]) lapply(DJList, FUN=plotDJ) } # ---------------------- calculating the clonality score ---------------------- if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available { clonalityFrame = inputdata if(clonaltype != "none"){ clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":")) clonalityFrame$ReplicateConcat = paste(clonalityFrame$clonaltype, clonalityFrame$Sample, clonalityFrame$Replicate, sep = ":") clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ] } write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T) ClonalitySampleReplicatePrint <- function(dat){ write.table(dat, paste("clonality_", unique(inputdata$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T) } clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")]) #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint) ClonalitySamplePrint <- function(dat){ write.table(dat, paste("clonality_", unique(inputdata$Sample) , ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T) } clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,"Sample"]) #lapply(clonalityFrameSplit, FUN=ClonalitySamplePrint) clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")]) clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")]) clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")]) clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample") ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15') tcct = textConnection(ct) CT = read.table(tcct, sep="\t", header=TRUE) close(tcct) clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T) clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")]) ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")]) clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads) ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads ReplicatePrint <- function(dat){ write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) } ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) lapply(ReplicateSplit, FUN=ReplicatePrint) ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")]) clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) ReplicateSumPrint <- function(dat){ write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) } ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")]) clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T) clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2) clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1) ClonalityScorePrint <- function(dat){ write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) } clonalityScore = clonalFreqCount[c("Sample", "Result")] clonalityScore = unique(clonalityScore) clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"]) lapply(clonalityScoreSplit, FUN=ClonalityScorePrint) clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")] ClonalityOverviewPrint <- function(dat){ write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) } clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample) lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint) } imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb") if(all(imgtcolumns %in% colnames(inputdata))) { newData = data.frame(data.table(PRODF)[,list(unique=.N, VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T), P1=mean(P3V.nt.nb, na.rm=T), N1=mean(N1.REGION.nt.nb, na.rm=T), P2=mean(P5D.nt.nb, na.rm=T), DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T), DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T), P3=mean(P3D.nt.nb, na.rm=T), N2=mean(N2.REGION.nt.nb, na.rm=T), P4=mean(P5J.nt.nb, na.rm=T), DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T), Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) + mean(X5D.REGION.trimmed.nt.nb, na.rm=T) + mean(X3D.REGION.trimmed.nt.nb, na.rm=T) + mean(X5J.REGION.trimmed.nt.nb, na.rm=T)), Total.N=( mean(N1.REGION.nt.nb, na.rm=T) + mean(N2.REGION.nt.nb, na.rm=T)), Total.P=( mean(P3V.nt.nb, na.rm=T) + mean(P5D.nt.nb, na.rm=T) + mean(P3D.nt.nb, na.rm=T) + mean(P5J.nt.nb, na.rm=T))), by=c("Sample")]) write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) newData = data.frame(data.table(UNPROD)[,list(unique=.N, VH.DEL=mean(X3V.REGION.trimmed.nt.nb, na.rm=T), P1=mean(P3V.nt.nb, na.rm=T), N1=mean(N1.REGION.nt.nb, na.rm=T), P2=mean(P5D.nt.nb, na.rm=T), DEL.DH=mean(X5D.REGION.trimmed.nt.nb, na.rm=T), DH.DEL=mean(X3D.REGION.trimmed.nt.nb, na.rm=T), P3=mean(P3D.nt.nb, na.rm=T), N2=mean(N2.REGION.nt.nb, na.rm=T), P4=mean(P5J.nt.nb, na.rm=T), DEL.JH=mean(X5J.REGION.trimmed.nt.nb, na.rm=T), Total.Del=( mean(X3V.REGION.trimmed.nt.nb, na.rm=T) + mean(X5D.REGION.trimmed.nt.nb, na.rm=T) + mean(X3D.REGION.trimmed.nt.nb, na.rm=T) + mean(X5J.REGION.trimmed.nt.nb, na.rm=T)), Total.N=( mean(N1.REGION.nt.nb, na.rm=T) + mean(N2.REGION.nt.nb, na.rm=T)), Total.P=( mean(P3V.nt.nb, na.rm=T) + mean(P5D.nt.nb, na.rm=T) + mean(P3D.nt.nb, na.rm=T) + mean(P5J.nt.nb, na.rm=T))), by=c("Sample")]) write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) }