Mercurial > repos > davidvanzessen > complete_immunerepertoire_igg
changeset 7:a9053212a462 draft
Uploaded
| author | davidvanzessen |
|---|---|
| date | Mon, 05 Jan 2015 09:30:08 -0500 |
| parents | 8b46fca04595 |
| children | 043fd6613fd9 |
| files | RScript.r RScript_b.r RScript_t.r asc.gif bg.gif complete.sh complete_immunerepertoire.xml desc.gif imgt_loader.py jquery.tablesorter.min.js r_wrapper.sh r_wrapper_b.sh r_wrapper_t.sh style.css |
| diffstat | 14 files changed, 754 insertions(+), 1337 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RScript.r Mon Jan 05 09:30:08 2015 -0500 @@ -0,0 +1,580 @@ +# ---------------------- 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 +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 +if(filterproductive){ + if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column + PRODF = 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$CDR3.Found.How != "NOT_FOUND" , ] + } +} + +#remove duplicates based on the clonaltype +if(clonaltype != "none"){ + PRODF$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 + +ct = unlist(strsplit(clonaltype, ",")) +if(clonaltype == "none"){ + ct = c("ID") +} + +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$Sample)) + + completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE) + completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE) + completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE) + DJList = split(completeDJ, f=completeDJ[,"Sample"]) + lapply(DJList, FUN=plotDJ) +} + + +# ---------------------- calculating the clonality score ---------------------- + +if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available +{ + clonalityFrame = inputdata + if(clonaltype != "none"){ + clonalityFrame$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(Reads), ReadsSquaredSum=sum(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(inputdata)[,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, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) +}
--- a/RScript_b.r Mon Sep 08 04:24:04 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,462 +0,0 @@ -#options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) - -args <- commandArgs(trailingOnly = TRUE) - -inFile = args[1] -outFile = args[2] -outDir = args[3] -clonalType = args[4] -species = args[5] -locus = args[6] -selection = args[7] - - - -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) - - -test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="") - -test = test[test$Sample != "",] - -test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene) -test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene) -test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene) - -#test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":")) -test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":")) - -PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ] -if("Functionality" %in% colnames(test)) { - PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ] -} - -NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ] - -#PRODF = PROD[ -1] - -PRODF = PROD - -#PRODF = unique(PRODF) - - - -if(selection == "unique"){ - PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ] -} - -PRODFV = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.V.Gene")]) -PRODFV$Length = as.numeric(PRODFV$Length) -Total = 0 -Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$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=.N), by=c("Sample", "Top.D.Gene")]) -PRODFD$Length = as.numeric(PRODFD$Length) -Total = 0 -Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length))) -PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE) -PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total)) - -PRODFJ = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Sample", "Top.J.Gene")]) -PRODFJ$Length = as.numeric(PRODFJ$Length) -Total = 0 -Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$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)) - -V = c("v.name\tchr.orderV") -D = c("v.name\tchr.orderD") -J = c("v.name\tchr.orderJ") - -if(species == "human"){ - 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 == "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 == "igk" || locus == "igl")){ - useD = FALSE -} - -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) - -setwd(outDir) - -write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T) - -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(); - -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) - -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) - -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$v.name, Sample = unique(test$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$v.name, Sample = unique(test$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$v.name, Sample = unique(test$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) -} - -sampleFile <- file("samples.txt") -un = unique(test$Sample) -un = paste(un, sep="\n") -writeLines(un, sampleFile) -close(sampleFile) - - -if("Replicate" %in% colnames(test)) -{ - clonalityFrame = PROD - clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":")) - clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ] - write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T) - - ClonalitySampleReplicatePrint <- function(dat){ - write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T) - } - - clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")]) - #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint) - - ClonalitySamplePrint <- function(dat){ - write.table(dat, paste("clonality_", unique(dat$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", "VDJCDR3")]) - 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", "VDJCDR3")]) - ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")]) - clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads) - ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads - - ReplicatePrint <- function(dat){ - write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSplit, FUN=ReplicatePrint) - - ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")]) - clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) - - - ReplicateSumPrint <- function(dat){ - write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) - - clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")]) - clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T) - clonalFreqCount$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) -} - -if("Functionality" %in% colnames(test)) -{ - newData = data.frame(data.table(PROD)[,list(unique=.N, - VH.DEL=mean(X3V.REGION.trimmed.nt.nb), - P1=mean(P3V.nt.nb), - N1=mean(N1.REGION.nt.nb), - P2=mean(P5D.nt.nb), - DEL.DH=mean(X5D.REGION.trimmed.nt.nb), - DH.DEL=mean(X3D.REGION.trimmed.nt.nb), - P3=mean(P3D.nt.nb), - N2=mean(N2.REGION.nt.nb), - P4=mean(P5J.nt.nb), - DEL.JH=mean(X5J.REGION.trimmed.nt.nb), - Total.Del=( mean(X3V.REGION.trimmed.nt.nb) + - mean(X5D.REGION.trimmed.nt.nb) + - mean(X3D.REGION.trimmed.nt.nb) + - mean(X5J.REGION.trimmed.nt.nb)), - - Total.N=( mean(N1.REGION.nt.nb) + - mean(N2.REGION.nt.nb)), - - Total.P=( mean(P3V.nt.nb) + - mean(P5D.nt.nb) + - mean(P3D.nt.nb) + - mean(P5J.nt.nb))), - by=c("Sample")]) - write.table(newData, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) -}
--- a/RScript_t.r Mon Sep 08 04:24:04 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,499 +0,0 @@ -#options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) - -args <- commandArgs(trailingOnly = TRUE) - -inFile = args[1] -outFile = args[2] -outDir = args[3] -clonalType = args[4] -species = args[5] -locus = args[6] -selection = args[7] - -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) - - -test = read.table(inFile, sep="\t", header=TRUE, fill=T, comment.char="") - -test = test[test$Sample != "",] - -print("test1\n") - -test$Top.V.Gene = gsub("[*]([0-9]+)", "", test$Top.V.Gene) -test$Top.D.Gene = gsub("[*]([0-9]+)", "", test$Top.D.Gene) -test$Top.J.Gene = gsub("[*]([0-9]+)", "", test$Top.J.Gene) - -#test$VDJCDR3 = do.call(paste, c(test[c("Top.V.Gene", "Top.D.Gene", "Top.J.Gene","CDR3.Seq.DNA")], sep = ":")) -test$VDJCDR3 = do.call(paste, c(test[unlist(strsplit(clonalType, ","))], sep = ":")) - -PROD = test[test$VDJ.Frame != "In-frame with stop codon" & test$VDJ.Frame != "Out-of-frame" & test$CDR3.Found.How != "NOT_FOUND" , ] -if("Functionality" %in% colnames(test)) { - PROD = test[test$Functionality == "productive" | test$Functionality == "productive (see comment)", ] -} - -NONPROD = test[test$VDJ.Frame == "In-frame with stop codon" | test$VDJ.Frame == "Out-of-frame" | test$CDR3.Found.How == "NOT_FOUND" , ] - -#PRODF = PROD[ -1] - -PRODF = PROD -print("test2\n") -#PRODF = unique(PRODF) -if(any(grepl(pattern="_", x=PRODF$ID))){ #dumb and way to simple - PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID) - PRODF$freq = gsub("_.*", "", PRODF$freq) - PRODF$freq = as.numeric(PRODF$freq) - if(any(is.na(PRODF$freq))){ #fix the dumbness if it fails - PRODF$freq = 1 - if(selection == "unique"){ - PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ] - } - } -} else { - PRODF$freq = 1 - if(selection == "unique"){ - PRODF = PRODF[!duplicated(PRODF$VDJCDR3), ] - } -} - -PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")]) -PRODFV$Length = as.numeric(PRODFV$Length) -Total = 0 -Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$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")]) -PRODFD$Length = as.numeric(PRODFD$Length) -Total = 0 -Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length))) -PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE) -PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total)) - -PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")]) -PRODFJ$Length = as.numeric(PRODFJ$Length) -Total = 0 -Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$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)) - -V = c("v.name\tchr.orderV\n") -D = c("v.name\tchr.orderD\n") -J = c("v.name\tchr.orderJ\n") - -print("test3\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 (species == "mouse"){ - if(locus == "trb"){ - cat("mouse trb not yet implemented") - } 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") - } -} -useD = TRUE -if(species == "human" && locus == "tra"){ - useD = FALSE - cat("No D Genes in this species/locus") -} - -print("test4\n") - -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) - -setwd(outDir) - -write.table(PRODF, "allUnique.csv", sep=",",quote=F,row.names=F,col.names=T) - -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(); - -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) -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(); - -print("test5\n") - -VGenes = PRODF[,c("Sample", "Top.V.Gene", "freq")] -VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene) -VGenes = data.frame(data.table(VGenes)[, list(Count=sum(freq)), 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) - -DGenes = PRODF[,c("Sample", "Top.D.Gene", "freq")] -DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene) -DGenes = data.frame(data.table(DGenes)[, list(Count=sum(freq)), 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") -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", "freq")] -JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene) -JGenes = data.frame(data.table(JGenes)[, list(Count=sum(freq)), 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) - -CDR3Length = data.frame(data.table(PRODF)[, list(Count=sum(freq)), 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) - -revVchain = Vchain -revDchain = Dchain -revVchain$chr.orderV = rev(revVchain$chr.orderV) -revDchain$chr.orderD = rev(revDchain$chr.orderD) - -print("test6\n") - -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=sum(freq)), 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$v.name, Sample = unique(test$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() - } - 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=sum(freq)), 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$v.name, Sample = unique(test$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) - -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$v.name, Sample = unique(test$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) - -sampleFile <- file("samples.txt") -un = unique(test$Sample) -un = paste(un, sep="\n") -writeLines(un, sampleFile) -close(sampleFile) - -print("test7\n") - -if("Replicate" %in% colnames(test)) -{ - clonalityFrame = PROD - clonalityFrame$ReplicateConcat = do.call(paste, c(clonalityFrame[c("VDJCDR3", "Sample", "Replicate")], sep = ":")) - clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$ReplicateConcat), ] - write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T) - - ClonalitySampleReplicatePrint <- function(dat){ - write.table(dat, paste("clonality_", unique(dat$Sample) , "_", unique(dat$Replicate), ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T) - } - - clonalityFrameSplit = split(clonalityFrame, f=clonalityFrame[,c("Sample", "Replicate")]) - #lapply(clonalityFrameSplit, FUN=ClonalitySampleReplicatePrint) - - ClonalitySamplePrint <- function(dat){ - write.table(dat, paste("clonality_", unique(dat$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", "VDJCDR3")]) - 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", "VDJCDR3")]) - ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")]) - clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads) - ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads - - ReplicatePrint <- function(dat){ - write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSplit, FUN=ReplicatePrint) - - ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(Reads), ReadsSquaredSum=sum(squared)), by=c("Sample")]) - clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T) - - - ReplicateSumPrint <- function(dat){ - write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F) - } - - ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"]) - lapply(ReplicateSumSplit, FUN=ReplicateSumPrint) - - writeClonalitySequences <- function(dat){ - for(i in c(2,3,4,5,6)){ - fltr = dat[dat$Type == i,] - if(length(fltr[,1]) == 0){ - next - } - tmp = clonalityFrame[clonalityFrame$Sample == fltr$Sample[1] & clonalityFrame$VDJCDR3 %in% fltr$VDJCDR3,] - tmp = tmp[order(tmp$VDJCDR3),] - write.table(tmp, paste("ClonalitySequences_", unique(dat[1])[1,1] , "_", i, ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=T) - } - } - freqsplt = split(clonalFreq[clonalFreq$Type > 1,], clonalFreq[clonalFreq$Type > 1,]$Sample) - lapply(freqsplt, FUN=writeClonalitySequences) - - 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) -} - -print("test8\n") - -if("Functionality" %in% colnames(test)) -{ - newData = data.frame(data.table(PROD)[,list(unique=.N, - VH.DEL=mean(X3V.REGION.trimmed.nt.nb), - P1=mean(P3V.nt.nb), - N1=mean(N1.REGION.nt.nb), - P2=mean(P5D.nt.nb), - DEL.DH=mean(X5D.REGION.trimmed.nt.nb), - DH.DEL=mean(X3D.REGION.trimmed.nt.nb), - P3=mean(P3D.nt.nb), - N2=mean(N2.REGION.nt.nb), - P4=mean(P5J.nt.nb), - DEL.JH=mean(X5J.REGION.trimmed.nt.nb), - Total.Del=( mean(X3V.REGION.trimmed.nt.nb) + - mean(X5D.REGION.trimmed.nt.nb) + - mean(X3D.REGION.trimmed.nt.nb) + - mean(X5J.REGION.trimmed.nt.nb)), - - Total.N=( mean(N1.REGION.nt.nb) + - mean(N2.REGION.nt.nb)), - - Total.P=( mean(P3V.nt.nb) + - mean(P5D.nt.nb) + - mean(P3D.nt.nb) + - mean(P5J.nt.nb))), - by=c("Sample")]) - write.table(newData, "junctionAnalysis.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F) -} - -print("test9\n")
--- a/complete.sh Mon Sep 08 04:24:04 2014 -0400 +++ b/complete.sh Mon Jan 05 09:30:08 2015 -0500 @@ -6,7 +6,7 @@ clonalType=$4 species=$5 locus=$6 -selection=$7 +filterproductive=$7 html=$2 dir="$(cd "$(dirname "$0")" && pwd)" @@ -23,7 +23,6 @@ echo "igblastn -germline_db_V $PWD/igblastdatabase/database/human_gl_V -germline_db_J $PWD/igblastdatabase/database/human_gl_J -germline_db_D $PWD/igblastdatabase/database/human_gl_D -domain_system imgt -query $1 -auxiliary_data $PWD/igblastdatabase/optional_file/human_gl.aux -show_translation -outfmt 3 > $PWD/$4" /home/galaxy/galaxy/igblast/igblastn -germline_db_V $PWD/igblastdatabase/database/human_gl_V -germline_db_J $PWD/igblastdatabase/database/human_gl_J -germline_db_D $PWD/igblastdatabase/database/human_gl_D -domain_system imgt -query $1 -auxiliary_data $PWD/igblastdatabase/optional_file/human_gl.aux -show_translation -outfmt 3 > $PWD/$4 echo "<tr><td>Finished blast of sample $3 of patient $2</td></tr>" >> $html - echo "<tr><td>Starting parse of sample $3 of patient $2</td></tr>" >> $html perl $dir/igparse.pl $PWD/$4 0 | grep -v "D:" | cut -f2- > "$5" echo "<tr><td>Finished parse of sample $3 of patient $2</td></tr>" >> $html @@ -33,7 +32,6 @@ echo "<tr><td>Starting imgt convert of sample $3 of patient $2</td></tr>" >> $html bash $dir/imgt_loader.sh $1 $4 $5 echo "<tr><td>Finished conversion of sample $3 of patient $2</td></tr>" >> $html - } id="" @@ -82,17 +80,5 @@ echo "after ED" -if [ "$locus" == "igh" ] || [ "$locus" == "igk" ] || [ "$locus" == "igl" ]; then - bash $dir/r_wrapper_b.sh $PWD/merged.txt $2 $outputDir $clonalType $species $locus $selection -else - bash $dir/r_wrapper_t.sh $PWD/merged.txt $2 $outputDir $clonalType $species $locus $selection -fi - +$dir/r_wrapper.sh $PWD/merged.txt $2 $outputDir $clonalType $species $locus $filterproductive - - - - - - -
--- a/complete_immunerepertoire.xml Mon Sep 08 04:24:04 2014 -0400 +++ b/complete_immunerepertoire.xml Mon Jan 05 09:30:08 2015 -0500 @@ -8,7 +8,7 @@ ${g.sample} #end for #end for -" $out_file $out_file.files_path "$clonaltype_select" $species $locus $selection +" $out_file $out_file.files_path "$clonaltype" $species $locus $filterproductive </command> <inputs> <repeat name="patients" title="Patients" min="1" default="1"> @@ -17,7 +17,8 @@ </repeat> <param name="id" type="text" label="ID" /> </repeat> - <param name="clonaltype_select" type="select" label="Clonal Type Definition"> + <param name="clonaltype" type="select" label="Clonal Type Definition"> + <option value="none">Don't remove duplicates based on clonaltype</option> <option value="Top.V.Gene,CDR3.Seq">Top.V.Gene, CDR3.Seq</option> <option value="Top.V.Gene,CDR3.Seq.DNA">Top.V.Gene, CDR3.Seq.DNA</option> <option value="Top.V.Gene,Top.J.Gene,CDR3.Seq">Top.V.Gene, Top.J.Gene, CDR3.Seq</option> @@ -39,10 +40,10 @@ <option value="trg">TRG</option> <option value="trd">TRD</option> </param> - - <param name="selection" type="select" label="Selection"> - <option value="unique">Unique (Based on clonaltype)</option> - <option value="all">All</option> + + <param name="filterproductive" type="select" label="Filter out the unproductive sequences"> + <option value="yes">Yes</option> + <option value="no">No</option> </param> </inputs> <outputs> @@ -52,4 +53,3 @@ The entire Immune Repertoire pipeline as a single tool, input several FASTA files, give them an ID and it will BLAST, parse, merge and plot them. </help> </tool> -
--- a/imgt_loader.py Mon Sep 08 04:24:04 2014 -0400 +++ b/imgt_loader.py Mon Jan 05 09:30:08 2015 -0500 @@ -128,7 +128,6 @@ outFrame["Top D Gene"] = outFrame["Top D Gene"].apply(lambda x: filterGenes(x, dPattern)) outFrame["Top J Gene"] = outFrame["Top J Gene"].apply(lambda x: filterGenes(x, jPattern)) -print outFrame tmp = outFrame["VDJ Frame"] tmp = tmp.replace("in-frame", "In-frame") @@ -137,6 +136,6 @@ outFrame["VDJ Frame"] = tmp outFrame["CDR3 Length DNA"] = outFrame["CDR3 Seq DNA"].map(str).map(len) safeLength = lambda x: len(x) if type(x) == str else 0 -outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows? +#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows? #outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top D Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows? outFrame.to_csv(outFile, sep="\t", index=False, index_label="index")
--- a/jquery.tablesorter.min.js Mon Sep 08 04:24:04 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,4 +0,0 @@ - -(function($){$.extend({tablesorter:new -function(){var parsers=[],widgets=[];this.defaults={cssHeader:"header",cssAsc:"headerSortUp",cssDesc:"headerSortDown",cssChildRow:"expand-child",sortInitialOrder:"asc",sortMultiSortKey:"shiftKey",sortForce:null,sortAppend:null,sortLocaleCompare:true,textExtraction:"simple",parsers:{},widgets:[],widgetZebra:{css:["even","odd"]},headers:{},widthFixed:false,cancelSelection:true,sortList:[],headerList:[],dateFormat:"us",decimal:'/\.|\,/g',onRenderHeader:null,selectorHeaders:'thead th',debug:false};function benchmark(s,d){log(s+","+(new Date().getTime()-d.getTime())+"ms");}this.benchmark=benchmark;function log(s){if(typeof console!="undefined"&&typeof console.debug!="undefined"){console.log(s);}else{alert(s);}}function buildParserCache(table,$headers){if(table.config.debug){var 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/r_wrapper.sh Mon Jan 05 09:30:08 2015 -0500 @@ -0,0 +1,164 @@ +#!/bin/bash + +inputFile=$1 +outputDir=$3 +outputFile=$3/index.html #$2 +clonalType=$4 +species=$5 +locus=$6 +filterproductive=$7 +useD="false" +if [[ "$species" == "human" && "$locus" = "igh" ]] ; then + useD="true" +fi +dir="$(cd "$(dirname "$0")" && pwd)" +mkdir $3 +Rscript --verbose $dir/RScript.r $inputFile $outputDir $outputDir $clonalType $species $locus $filterproductive 2>&1 +cp $dir/tabber.js $outputDir +cp $dir/style.css $outputDir +cp $dir/script.js $outputDir +cp $dir/jquery-1.11.0.min.js $outputDir +samples=`cat $outputDir/samples.txt` +echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)<br />" > $2 +echo "<table border = 1>" >> $2 +echo "<thead><tr><th>Sample/Replicate</th><th>All</th><th>Productive</th><th>Unique Productive</th><th>Unproductive</th><th>Unique Unproductive</th></tr></thead>" >> $2 +while IFS=, read sample all productive perc_prod productive_unique perc_prod_un unproductive perc_unprod unproductive_unique perc_unprod_un + do + echo "<tr><td>$sample</td>" >> $2 + echo "<td>$all</td>" >> $2 + echo "<td>$productive (${perc_prod}%)</td>" >> $2 + echo "<td>$productive_unique (${perc_prod_un}%)</td>" >> $2 + echo "<td>$unproductive (${perc_unprod}%)</td>" >> $2 + echo "<td>$unproductive_unique (${perc_unprod_un}%)</td></tr>" >> $2 +done < $outputDir/productive_counting.txt +echo "</table border></center></html>" >> $2 + +echo "productive_counting.txt" +echo "<html><head><title>Report on:" >> $outputFile +for sample in $samples; do + echo " $sample" >> $outputFile +done +echo "</title><script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $outputFile +echo "<script type='text/javascript' src='tabber.js'></script>" >> $outputFile +echo "<script type='text/javascript' src='script.js'></script>" >> $outputFile +echo "<link rel='stylesheet' type='text/css' href='style.css'></head>" >> $outputFile +echo "<div class='tabber'><div class='tabbertab' title='Gene frequencies'>" >> $outputFile + +echo "<img src='CDR3LengthPlot.png'/><br />" >> $outputFile +echo "<img src='VFPlot.png'/>" >> $outputFile +if [[ "$useD" == "true" ]] ; then + echo "<img src='DFPlot.png'/>" >> $outputFile +fi +echo "<img src='JFPlot.png'/>" >> $outputFile +echo "<img src='VPlot.png'/>" >> $outputFile +if [[ "$useD" == "true" ]] ; then + echo "<img src='DPlot.png'/>" >> $outputFile +fi +echo "<img src='JPlot.png'/></div>" >> $outputFile + +count=1 +echo "<div class='tabbertab' title='Heatmaps'><div class='tabber'>" >> $outputFile +for sample in $samples; do + echo "<div class='tabbertab' title='$sample'><table border='1'><tr>" >> $outputFile + if [[ "$useD" == "true" ]] ; then + echo "<td><img src='HeatmapVD_$sample.png'/></td>" >> $outputFile + fi + echo "<td><img src='HeatmapVJ_$sample.png'/></td>" >> $outputFile + if [[ "$useD" == "true" ]] ; then + echo "<td><img src='HeatmapDJ_$sample.png'/></td>" >> $outputFile + fi + echo "</tr></table></div>" >> $outputFile + count=$((count+1)) +done +echo "</div></div>" >> $outputFile + +#echo "<div class='tabbertab' title='Interactive'><svg class='chart'></svg><script src='http://d3js.org/d3.v3.min.js'></script></div>" >> $outputFile + +hasReplicateColumn="$(if head -n 1 $inputFile | grep -q 'Replicate'; then echo 'Yes'; else echo 'No'; fi)" +echo "$hasReplicateColumn" +#if its a 'new' merged file with replicate info +if [[ "$hasReplicateColumn" == "Yes" ]] ; then + echo "<div class='tabbertab' title='Clonality'><div class='tabber'>" >> $outputFile + for sample in $samples; do + clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)" + echo "<div class='tabbertab' title='$sample'><table border='1'>" >> $outputFile + echo "<tr><td colspan='4'>Clonality Score: $clonalityScore</td></tr>" >> $outputFile + + #replicate,reads,squared + echo "<tr><td>Replicate ID</td><td>Number of Reads</td><td>Reads Squared</td><td></td></tr>" >> $outputFile + while IFS=, read replicate reads squared + do + + echo "<tr><td>$replicate</td><td>$reads</td><td>$squared</td><td></td></tr>" >> $outputFile + done < $outputDir/ReplicateReads_$sample.csv + + #sum of reads and reads squared + while IFS=, read readsSum squaredSum + do + echo "<tr><td>Sum</td><td>$readsSum</td><td>$squaredSum</td></tr>" >> $outputFile + done < $outputDir/ReplicateSumReads_$sample.csv + + #overview + echo "<tr><td>Coincidence Type</td><td>Raw Coincidence Freq</td><td>Coincidence Weight</td><td>Coincidences, Weighted</td></tr>" >> $outputFile + while IFS=, read type count weight weightedCount + do + echo "<tr><td>$type</td><td>$count</td><td>$weight</td><td>$weightedCount</td></tr>" >> $outputFile + done < $outputDir/ClonalityOverView_$sample.csv + echo "</table></div>" >> $outputFile + done + echo "</div></div>" >> $outputFile +fi + +hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)" + +if [[ "$hasJunctionData" == "Yes" ]] ; then + echo "<div class='tabbertab' title='Junction Analysis'><table border='1' id='junction_table'><thead><tr><th>Sample</th><th>unique</th><th>VH.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.DH</th><th>DH.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.JH</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><thead></tr><tbody>" >> $outputFile + while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP + do + echo "<tr><td>$Sample</td><td>$unique</td><td>$VHDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELDH</td><td>$DHDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJH</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td></tr>" >> $outputFile + done < $outputDir/junctionAnalysis.csv + echo "</tbody></table></div>" >> $outputFile +fi + +echo "<div class='tabbertab' title='Comparison'><table border='1'><tr><th>ID</th><th>Include</th></tr>" >> $outputFile +for sample in $samples; do + echo "<tr><td>$sample</td><td><input type='checkbox' onchange=\"javascript:compareAdd('$sample')\" id='compare_checkbox_$sample'/></td></tr>" >> $outputFile +done +echo "</table><div name='comparisonarea'>" >> $outputFile +echo "<table><tr id='comparison_table_vd'></tr></table>" >> $outputFile +echo "<table><tr id='comparison_table_vj'></tr></table>" >> $outputFile +echo "<table><tr id='comparison_table_dj'></tr></table>" >> $outputFile +echo "</div></div>" >> $outputFile + +echo "<div class='tabbertab' title='Downloads'>" >> $outputFile +echo "<table border='1'>" >> $outputFile +echo "<tr><th>Description</th><th>Link</th></tr>" >> $outputFile +echo "<tr><td>The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType)</td><td><a href='allUnique.csv'>Download</a></td></tr>" >> $outputFile +echo "<tr><td>The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType)</td><td><a href='clonalityComplete.csv'>Download</a></td></tr>" >> $outputFile + +echo "<tr><td>The dataset used to generate the CDR3 length frequency graph</td><td><a href='CDR3LengthPlot.csv'>Download</a></td></tr>" >> $outputFile + +echo "<tr><td>The dataset used to generate the V gene family frequency graph</td><td><a href='VFFrequency.csv'>Download</a></td></tr>" >> $outputFile +if [[ "$useD" == "true" ]] ; then + echo "<tr><td>The dataset used to generate the D gene family frequency graph</td><td><a href='DFFrequency.csv'>Download</a></td></tr>" >> $outputFile +fi +echo "<tr><td>The dataset used to generate the J gene family frequency graph</td><td><a href='JFFrequency.csv'>Download</a></td></tr>" >> $outputFile + +echo "<tr><td>The dataset used to generate the V gene frequency graph</td><td><a href='VFrequency.csv'>Download</a></td></tr>" >> $outputFile +if [[ "$useD" == "true" ]] ; then + echo "<tr><td>The dataset used to generate the D gene frequency graph</td><td><a href='DFrequency.csv'>Download</a></td></tr>" >> $outputFile +fi +echo "<tr><td>The dataset used to generate the J gene frequency graph</td><td><a href='JFrequency.csv'>Download</a></td></tr>" >> $outputFile + +for sample in $samples; do + if [[ "$useD" == "true" ]] ; then + echo "<tr><td>The data used to generate the VD heatmap for $sample.</td><td><a href='HeatmapVD_$sample.csv'>Download</a></td></tr>" >> $outputFile + fi + echo "<tr><td>The data used to generate the VJ heatmap for $sample.</td><td><a href='HeatmapVJ_$sample.csv'>Download</a></td></tr>" >> $outputFile + if [[ "$useD" == "true" ]] ; then + echo "<tr><td>The data used to generate the DJ heatmap for $sample.</td><td><a href='HeatmapDJ_$sample.csv'>Download</a></td></tr>" >> $outputFile + fi +done + +echo "</table>" >> $outputFile +echo "</div></html>" >> $outputFile
--- a/r_wrapper_b.sh Mon Sep 08 04:24:04 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,156 +0,0 @@ -#!/bin/bash - -inputFile=$1 -outputDir=$3 -outputFile=$3/index.html #$2 -clonalType=$4 -species=$5 -locus=$6 -selection=$7 - -useD="false" -if [[ "$species" == "human" && "$locus" = "igh" ]] ; then - useD="true" -fi -dir="$(cd "$(dirname "$0")" && pwd)" -mkdir $3 -Rscript --verbose $dir/RScript_b.r $inputFile $outputDir $outputDir $clonalType $species $locus $selection 2>&1 -cp $dir/tabber.js $outputDir -cp $dir/style.css $outputDir -cp $dir/script.js $outputDir -cp $dir/jquery-1.11.0.min.js $outputDir -cp $dir/jquery.tablesorter.min.js $outputDir -cp $dir/asc.gif $outputDir -cp $dir/desc.gif $outputDir -cp $dir/bg.gif $outputDir -samples=`cat $outputDir/samples.txt` -echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)</center></html>" > $2 -echo "<html><head><title>Report on:" >> $outputFile -for sample in $samples; do - echo " $sample" >> $outputFile -done -echo "</title><script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='tabber.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='script.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='jquery.tablesorter.min.js'></script>" >> $outputFile -echo "<link rel='stylesheet' type='text/css' href='style.css'></head>" >> $outputFile -echo "<div class='tabber'><div class='tabbertab' title='Gene frequencies'>" >> $outputFile - -echo "<img src='CDR3LengthPlot.png'/><br />" >> $outputFile -echo "<img src='VFPlot.png'/>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<img src='DFPlot.png'/>" >> $outputFile -fi -echo "<img src='JFPlot.png'/>" >> $outputFile -echo "<img src='VPlot.png'/>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<img src='DPlot.png'/>" >> $outputFile -fi -echo "<img src='JPlot.png'/></div>" >> $outputFile - -count=1 -echo "<div class='tabbertab' title='Heatmaps'><div class='tabber'>" >> $outputFile -for sample in $samples; do - echo "<div class='tabbertab' title='$sample'><table border='1'><tr>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<td><img src='HeatmapVD_$sample.png'/></td>" >> $outputFile - fi - echo "<td><img src='HeatmapVJ_$sample.png'/></td>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<td><img src='HeatmapDJ_$sample.png'/></td>" >> $outputFile - fi - echo "</tr></table></div>" >> $outputFile - count=$((count+1)) -done -echo "</div></div>" >> $outputFile - -#echo "<div class='tabbertab' title='Interactive'><svg class='chart'></svg><script src='http://d3js.org/d3.v3.min.js'></script></div>" >> $outputFile - -hasReplicateColumn="$(if head -n 1 $inputFile | grep -q 'Replicate'; then echo 'Yes'; else echo 'No'; fi)" -echo "$hasReplicateColumn" -#if its a 'new' merged file with replicate info -if [[ "$hasReplicateColumn" == "Yes" ]] ; then - echo "<div class='tabbertab' title='Clonality'><div class='tabber'>" >> $outputFile - for sample in $samples; do - clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)" - echo "<div class='tabbertab' title='$sample'><table border='1'>" >> $outputFile - echo "<tr><td colspan='4'>Clonality Score: $clonalityScore</td></tr>" >> $outputFile - - #replicate,reads,squared - echo "<tr><td>Replicate ID</td><td>Number of Reads</td><td>Reads Squared</td><td></td></tr>" >> $outputFile - while IFS=, read replicate reads squared - do - - echo "<tr><td>$replicate</td><td>$reads</td><td>$squared</td><td></td></tr>" >> $outputFile - done < $outputDir/ReplicateReads_$sample.csv - - #sum of reads and reads squared - while IFS=, read readsSum squaredSum - do - echo "<tr><td>Sum</td><td>$readsSum</td><td>$squaredSum</td></tr>" >> $outputFile - done < $outputDir/ReplicateSumReads_$sample.csv - - #overview - echo "<tr><td>Coincidence Type</td><td>Raw Coincidence Freq</td><td>Coincidence Weight</td><td>Coincidences, Weighted</td></tr>" >> $outputFile - while IFS=, read type count weight weightedCount - do - echo "<tr><td>$type</td><td>$count</td><td>$weight</td><td>$weightedCount</td></tr>" >> $outputFile - done < $outputDir/ClonalityOverView_$sample.csv - echo "</table></div>" >> $outputFile - done - echo "</div></div>" >> $outputFile -fi - -hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)" - -if [[ "$hasJunctionData" == "Yes" ]] ; then - echo "<div class='tabbertab' title='Junction Analysis'><table border='1' id='junction_table' class='tablesorter'><thead><tr><th>Sample</th><th>unique</th><th>VH.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.DH</th><th>DH.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.JH</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><thead></tr><tbody>" >> $outputFile - while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP - do - echo "<tr><td>$Sample</td><td>$unique</td><td>$VHDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELDH</td><td>$DHDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJH</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td></tr>" >> $outputFile - done < $outputDir/junctionAnalysis.csv - echo "</tbody></table></div>" >> $outputFile -fi - -echo "<div class='tabbertab' title='Comparison'><table border='1'><tr><th>ID</th><th>Include</th></tr>" >> $outputFile -for sample in $samples; do - echo "<tr><td>$sample</td><td><input type='checkbox' onchange=\"javascript:compareAdd('$sample')\" id='compare_checkbox_$sample'/></td></tr>" >> $outputFile -done -echo "</table><div name='comparisonarea'>" >> $outputFile -echo "<table><tr id='comparison_table_vd'></tr></table>" >> $outputFile -echo "<table><tr id='comparison_table_vj'></tr></table>" >> $outputFile -echo "<table><tr id='comparison_table_dj'></tr></table>" >> $outputFile -echo "</div></div>" >> $outputFile - -echo "<div class='tabbertab' title='Downloads'>" >> $outputFile -echo "<table border='1'>" >> $outputFile -echo "<tr><th>Description</th><th>Link</th></tr>" >> $outputFile -echo "<tr><td>The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType)</td><td><a href='allUnique.csv'>Download</a></td></tr>" >> $outputFile -echo "<tr><td>The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType)</td><td><a href='clonalityComplete.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the CDR3 length frequency graph</td><td><a href='CDR3LengthPlot.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the V gene family frequency graph</td><td><a href='VFFrequency.csv'>Download</a></td></tr>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The dataset used to generate the D gene family frequency graph</td><td><a href='DFFrequency.csv'>Download</a></td></tr>" >> $outputFile -fi -echo "<tr><td>The dataset used to generate the J gene family frequency graph</td><td><a href='JFFrequency.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the V gene frequency graph</td><td><a href='VFrequency.csv'>Download</a></td></tr>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The dataset used to generate the D gene frequency graph</td><td><a href='DFrequency.csv'>Download</a></td></tr>" >> $outputFile -fi -echo "<tr><td>The dataset used to generate the J gene frequency graph</td><td><a href='JFrequency.csv'>Download</a></td></tr>" >> $outputFile - -for sample in $samples; do - if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The data used to generate the VD heatmap for $sample.</td><td><a href='HeatmapVD_$sample.csv'>Download</a></td></tr>" >> $outputFile - fi - echo "<tr><td>The data used to generate the VJ heatmap for $sample.</td><td><a href='HeatmapVJ_$sample.csv'>Download</a></td></tr>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The data used to generate the DJ heatmap for $sample.</td><td><a href='HeatmapDJ_$sample.csv'>Download</a></td></tr>" >> $outputFile - fi -done - -echo "</table>" >> $outputFile -echo "</div></html>" >> $outputFile
--- a/r_wrapper_t.sh Mon Sep 08 04:24:04 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,151 +0,0 @@ -#!/bin/bash - -inputFile=$1 -outputDir=$3 -outputFile=$3/index.html #$2 -clonalType=$4 -species=$5 -locus=$6 -selection=$7 -useD="true" -if [[ "$species" == "human" && "$locus" = "tra" ]] ; then - useD="false" -fi -dir="$(cd "$(dirname "$0")" && pwd)" -mkdir $3 -Rscript --verbose $dir/RScript_t.r $inputFile $outputDir $outputDir $clonalType $species $locus $selection 2>&1 -cp $dir/tabber.js $outputDir -cp $dir/style.css $outputDir -cp $dir/script.js $outputDir -cp $dir/jquery-1.11.0.min.js $outputDir -cp $dir/jquery.tablesorter.min.js $outputDir -cp $dir/asc.gif $outputDir -cp $dir/desc.gif $outputDir -cp $dir/bg.gif $outputDir -echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)</center></html>" > $2 -echo "<html<head>" >> $outputFile -echo "<script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='tabber.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='script.js'></script>" >> $outputFile -echo "<script type='text/javascript' src='jquery.tablesorter.min.js'></script>" >> $outputFile -echo "<link rel='stylesheet' type='text/css' href='style.css'></head>" >> $outputFile -echo "<div class='tabber'><div class='tabbertab' title='Gene frequencies'>" >> $outputFile - -echo "<img src='CDR3LengthPlot.png'/><br />" >> $outputFile -echo "<img src='VFPlot.png'/>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<img src='DFPlot.png'/>" >> $outputFile -fi -echo "<img src='JFPlot.png'/>" >> $outputFile -echo "<img src='VPlot.png'/>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<img src='DPlot.png'/>" >> $outputFile -fi -echo "<img src='JPlot.png'/></div>" >> $outputFile - -samples=`cat $outputDir/samples.txt` -count=1 -echo "<div class='tabbertab' title='Heatmaps'><div class='tabber'>" >> $outputFile -for sample in $samples; do - echo "<div class='tabbertab' title='$sample'><table border='1'><tr>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<td><img src='HeatmapVD_$sample.png'/></td>" >> $outputFile - fi - echo "<td><img src='HeatmapVJ_$sample.png'/></td>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<td><img src='HeatmapDJ_$sample.png'/></td>" >> $outputFile - fi - echo "</tr></table></div>" >> $outputFile - count=$((count+1)) -done -echo "</div></div>" >> $outputFile - - -hasReplicateColumn="$(if head -n 1 $inputFile | grep -q 'Replicate'; then echo 'Yes'; else echo 'No'; fi)" -echo "$hasReplicateColumn" -#if its a 'new' merged file with replicate info -if [[ "$hasReplicateColumn" == "Yes" ]] ; then - echo "<div class='tabbertab' title='Clonality'><div class='tabber'>" >> $outputFile - for sample in $samples; do - clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)" - echo "<div class='tabbertab' title='$sample'><table border='1'>" >> $outputFile - echo "<tr><td colspan='4'>Clonality Score: $clonalityScore</td></tr>" >> $outputFile - - #replicate,reads,squared - echo "<tr><td>Replicate ID</td><td>Number of Reads</td><td>Reads Squared</td><td></td></tr>" >> $outputFile - while IFS=, read replicate reads squared - do - - echo "<tr><td>$replicate</td><td>$reads</td><td>$squared</td><td></td></tr>" >> $outputFile - done < $outputDir/ReplicateReads_$sample.csv - - #sum of reads and reads squared - while IFS=, read readsSum squaredSum - do - echo "<tr><td>Sum</td><td>$readsSum</td><td>$squaredSum</td></tr>" >> $outputFile - done < $outputDir/ReplicateSumReads_$sample.csv - - #overview - echo "<tr><td>Coincidence Type</td><td>Raw Coincidence Freq</td><td>Coincidence Weight</td><td>Coincidences, Weighted</td></tr>" >> $outputFile - while IFS=, read type count weight weightedCount - do - echo "<tr><td><a href='ClonalitySequences_${sample}_${type}.csv'>$type</a></td><td>$count</td><td>$weight</td><td>$weightedCount</td></tr>" >> $outputFile - done < $outputDir/ClonalityOverView_$sample.csv - echo "</table></div>" >> $outputFile - done - echo "</div></div>" >> $outputFile -fi - -hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)" - -if [[ "$hasJunctionData" == "Yes" ]] ; then - echo "<div class='tabbertab' title='Junction Analysis'><table border='1' id='junction_table' class='tablesorter'><thead><tr><th>Sample</th><th>unique</th><th>VH.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.DH</th><th>DH.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.JH</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><thead></tr><tbody>" >> $outputFile - while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP - do - echo "<tr><td>$Sample</td><td>$unique</td><td>$VHDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELDH</td><td>$DHDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJH</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td></tr>" >> $outputFile - done < $outputDir/junctionAnalysis.csv - echo "</tbody></table></div>" >> $outputFile -fi - -echo "<div class='tabbertab' title='Comparison'><table border='1'><tr><th>ID</th><th>Include</th></tr>" >> $outputFile -for sample in $samples; do - echo "<tr><td>$sample</td><td><input type='checkbox' onchange=\"javascript:compareAdd('$sample')\" id='compare_checkbox_$sample'/></td></tr>" >> $outputFile -done -echo "</table><div name='comparisonarea'>" >> $outputFile -echo "<table><tr id='comparison_table_vd'></tr></table>" >> $outputFile -echo "<table><tr id='comparison_table_vj'></tr></table>" >> $outputFile -echo "<table><tr id='comparison_table_dj'></tr></table>" >> $outputFile -echo "</div></div>" >> $outputFile - -echo "<div class='tabbertab' title='Downloads'>" >> $outputFile -echo "<table border='1'>" >> $outputFile -echo "<tr><th>Description</th><th>Link</th></tr>" >> $outputFile -echo "<tr><td>The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType)</td><td><a href='allUnique.csv'>Download</a></td></tr>" >> $outputFile -echo "<tr><td>The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType)</td><td><a href='clonalityComplete.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the CDR3 length frequency graph</td><td><a href='CDR3LengthPlot.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the V gene family frequency graph</td><td><a href='VFFrequency.csv'>Download</a></td></tr>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The dataset used to generate the D gene family frequency graph</td><td><a href='DFFrequency.csv'>Download</a></td></tr>" >> $outputFile -fi -echo "<tr><td>The dataset used to generate the J gene family frequency graph</td><td><a href='JFFrequency.csv'>Download</a></td></tr>" >> $outputFile - -echo "<tr><td>The dataset used to generate the V gene frequency graph</td><td><a href='VFrequency.csv'>Download</a></td></tr>" >> $outputFile -if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The dataset used to generate the D gene frequency graph</td><td><a href='DFrequency.csv'>Download</a></td></tr>" >> $outputFile -fi -echo "<tr><td>The dataset used to generate the J gene frequency graph</td><td><a href='JFrequency.csv'>Download</a></td></tr>" >> $outputFile - -for sample in $samples; do - if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The data used to generate the VD heatmap for $sample.</td><td><a href='HeatmapVD_$sample.csv'>Download</a></td></tr>" >> $outputFile - fi - echo "<tr><td>The data used to generate the VJ heatmap for $sample.</td><td><a href='HeatmapVJ_$sample.csv'>Download</a></td></tr>" >> $outputFile - if [[ "$useD" == "true" ]] ; then - echo "<tr><td>The data used to generate the DJ heatmap for $sample.</td><td><a href='HeatmapDJ_$sample.csv'>Download</a></td></tr>" >> $outputFile - fi -done - -echo "</table>" >> $outputFile -echo "</div></html>" >> $outputFile
--- a/style.css Mon Sep 08 04:24:04 2014 -0400 +++ b/style.css Mon Jan 05 09:30:08 2015 -0500 @@ -108,43 +108,3 @@ overflow:auto; } -/* tables */ -table.tablesorter { - font-family:arial; - background-color: #CDCDCD; - margin:10px 0pt 15px; - font-size: 8pt; - width: 100%; - text-align: left; -} -table.tablesorter thead tr th, table.tablesorter tfoot tr th { - background-color: #e6EEEE; - border: 1px solid #FFF; - font-size: 8pt; - padding: 4px; -} -table.tablesorter thead tr .header { - background-image: url(bg.gif); - background-repeat: no-repeat; - background-position: center right; - cursor: pointer; -} -table.tablesorter tbody td { - color: #3D3D3D; - padding: 4px; - background-color: #FFF; - vertical-align: top; -} -table.tablesorter tbody tr.odd td { - background-color:#F0F0F6; -} -table.tablesorter thead tr .headerSortUp { - background-image: url(asc.gif); -} -table.tablesorter thead tr .headerSortDown { - background-image: url(desc.gif); -} -table.tablesorter thead tr .headerSortDown, table.tablesorter thead tr .headerSortUp { -background-color: #8dbdd8; -} -