# HG changeset patch
# User davidvanzessen
# Date 1420468208 18000
# Node ID a9053212a4622b38f3516caa258aae91a3b75b7b
# Parent 8b46fca0459548608e3120c886600511d3532a1d
Uploaded
diff -r 8b46fca04595 -r a9053212a462 RScript.r
--- /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)
+}
diff -r 8b46fca04595 -r a9053212a462 RScript_b.r
--- 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)
-}
diff -r 8b46fca04595 -r a9053212a462 RScript_t.r
--- 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")
diff -r 8b46fca04595 -r a9053212a462 asc.gif
Binary file asc.gif has changed
diff -r 8b46fca04595 -r a9053212a462 bg.gif
Binary file bg.gif has changed
diff -r 8b46fca04595 -r a9053212a462 complete.sh
--- 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 "
Finished blast of sample $3 of patient $2 |
" >> $html
-
echo "Starting parse of sample $3 of patient $2 |
" >> $html
perl $dir/igparse.pl $PWD/$4 0 | grep -v "D:" | cut -f2- > "$5"
echo "Finished parse of sample $3 of patient $2 |
" >> $html
@@ -33,7 +32,6 @@
echo "Starting imgt convert of sample $3 of patient $2 |
" >> $html
bash $dir/imgt_loader.sh $1 $4 $5
echo "Finished conversion of sample $3 of patient $2 |
" >> $html
-
}
id=""
@@ -82,17 +80,5 @@
echo "after ED"
-if [ "$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
-
-
-
-
-
-
-
diff -r 8b46fca04595 -r a9053212a462 complete_immunerepertoire.xml
--- 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 $filterproductive
@@ -17,7 +17,8 @@
-
+
+
@@ -39,10 +40,10 @@
-
-
-
-
+
+
+
+
@@ -52,4 +53,3 @@
The entire Immune Repertoire pipeline as a single tool, input several FASTA files, give them an ID and it will BLAST, parse, merge and plot them.
-
diff -r 8b46fca04595 -r a9053212a462 desc.gif
Binary file desc.gif has changed
diff -r 8b46fca04595 -r a9053212a462 imgt_loader.py
--- a/imgt_loader.py Mon Sep 08 04:24:04 2014 -0400
+++ b/imgt_loader.py Mon Jan 05 09:30:08 2015 -0500
@@ -128,7 +128,6 @@
outFrame["Top D Gene"] = outFrame["Top D Gene"].apply(lambda x: filterGenes(x, dPattern))
outFrame["Top J Gene"] = outFrame["Top J Gene"].apply(lambda x: filterGenes(x, jPattern))
-print outFrame
tmp = outFrame["VDJ Frame"]
tmp = tmp.replace("in-frame", "In-frame")
@@ -137,6 +136,6 @@
outFrame["VDJ Frame"] = tmp
outFrame["CDR3 Length DNA"] = outFrame["CDR3 Seq DNA"].map(str).map(len)
safeLength = lambda x: len(x) if type(x) == str else 0
-outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
+#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top D Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
outFrame.to_csv(outFile, sep="\t", index=False, index_label="index")
diff -r 8b46fca04595 -r a9053212a462 jquery.tablesorter.min.js
--- a/jquery.tablesorter.min.js Mon Sep 08 04:24:04 2014 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,4 +0,0 @@
-
-(function($){$.extend({tablesorter:new
-function(){var parsers=[],widgets=[];this.defaults={cssHeader:"header",cssAsc:"headerSortUp",cssDesc:"headerSortDown",cssChildRow:"expand-child",sortInitialOrder:"asc",sortMultiSortKey:"shiftKey",sortForce:null,sortAppend:null,sortLocaleCompare:true,textExtraction:"simple",parsers:{},widgets:[],widgetZebra:{css:["even","odd"]},headers:{},widthFixed:false,cancelSelection:true,sortList:[],headerList:[],dateFormat:"us",decimal:'/\.|\,/g',onRenderHeader:null,selectorHeaders:'thead th',debug:false};function benchmark(s,d){log(s+","+(new Date().getTime()-d.getTime())+"ms");}this.benchmark=benchmark;function log(s){if(typeof console!="undefined"&&typeof console.debug!="undefined"){console.log(s);}else{alert(s);}}function buildParserCache(table,$headers){if(table.config.debug){var parsersDebug="";}if(table.tBodies.length==0)return;var rows=table.tBodies[0].rows;if(rows[0]){var list=[],cells=rows[0].cells,l=cells.length;for(var i=0;i1){arr=arr.concat(checkCellColSpan(table,headerArr,row++));}else{if(table.tHead.length==1||(cell.rowSpan>1||!r[row+1])){arr.push(cell);}}}return arr;};function checkHeaderMetadata(cell){if(($.metadata)&&($(cell).metadata().sorter===false)){return true;};return false;}function checkHeaderOptions(table,i){if((table.config.headers[i])&&(table.config.headers[i].sorter===false)){return true;};return false;}function checkHeaderOptionsSortingLocked(table,i){if((table.config.headers[i])&&(table.config.headers[i].lockedOrder))return table.config.headers[i].lockedOrder;return false;}function applyWidget(table){var c=table.config.widgets;var l=c.length;for(var i=0;i');$("tr:first td",table.tBodies[0]).each(function(){colgroup.append($('').css('width',$(this).width()));});$(table).prepend(colgroup);};}function updateHeaderSortCount(table,sortList){var c=table.config,l=sortList.length;for(var i=0;i b["+i+"]) ? 1 : 0));";};function makeSortTextDesc(i){return"((b["+i+"] < a["+i+"]) ? -1 : ((b["+i+"] > a["+i+"]) ? 1 : 0));";};function makeSortNumeric(i){return"a["+i+"]-b["+i+"];";};function makeSortNumericDesc(i){return"b["+i+"]-a["+i+"];";};function sortText(a,b){if(table.config.sortLocaleCompare)return a.localeCompare(b);return((ab)?1:0));};function sortTextDesc(a,b){if(table.config.sortLocaleCompare)return b.localeCompare(a);return((ba)?1:0));};function sortNumeric(a,b){return a-b;};function sortNumericDesc(a,b){return b-a;};function getCachedSortType(parsers,i){return parsers[i].type;};this.construct=function(settings){return this.each(function(){if(!this.tHead||!this.tBodies)return;var $this,$document,$headers,cache,config,shiftDown=0,sortOrder;this.config={};config=$.extend(this.config,$.tablesorter.defaults,settings);$this=$(this);$.data(this,"tablesorter",config);$headers=buildHeaders(this);this.config.parsers=buildParserCache(this,$headers);cache=buildCache(this);var sortCSS=[config.cssDesc,config.cssAsc];fixColumnWidth(this);$headers.click(function(e){var totalRows=($this[0].tBodies[0]&&$this[0].tBodies[0].rows.length)||0;if(!this.sortDisabled&&totalRows>0){$this.trigger("sortStart");var $cell=$(this);var i=this.column;this.order=this.count++%2;if(this.lockedOrder)this.order=this.lockedOrder;if(!e[config.sortMultiSortKey]){config.sortList=[];if(config.sortForce!=null){var a=config.sortForce;for(var j=0;j0){$this.trigger("sorton",[config.sortList]);}applyWidget(this);});};this.addParser=function(parser){var l=parsers.length,a=true;for(var i=0;i&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 "Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)
" > $2
+echo "" >> $2
+echo "Sample/Replicate | All | Productive | Unique Productive | Unproductive | Unique Unproductive |
" >> $2
+while IFS=, read sample all productive perc_prod productive_unique perc_prod_un unproductive perc_unprod unproductive_unique perc_unprod_un
+ do
+ echo "$sample | " >> $2
+ echo "$all | " >> $2
+ echo "$productive (${perc_prod}%) | " >> $2
+ echo "$productive_unique (${perc_prod_un}%) | " >> $2
+ echo "$unproductive (${perc_unprod}%) | " >> $2
+ echo "$unproductive_unique (${perc_unprod_un}%) |
" >> $2
+done < $outputDir/productive_counting.txt
+echo "
" >> $2
+
+echo "productive_counting.txt"
+echo "Report on:" >> $outputFile
+for sample in $samples; do
+ echo " $sample" >> $outputFile
+done
+echo "" >> $outputFile
+echo "" >> $outputFile
+echo "" >> $outputFile
+echo "" >> $outputFile
+echo "" >> $outputFile
+
+echo "

" >> $outputFile
+echo "

" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+ echo "

" >> $outputFile
+fi
+echo "

" >> $outputFile
+echo "

" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+ echo "

" >> $outputFile
+fi
+echo "

" >> $outputFile
+
+count=1
+echo "
" >> $outputFile
+for sample in $samples; do
+ echo "
" >> $outputFile
+ if [[ "$useD" == "true" ]] ; then
+ echo " | " >> $outputFile
+ fi
+ echo " | " >> $outputFile
+ if [[ "$useD" == "true" ]] ; then
+ echo " | " >> $outputFile
+ fi
+ echo "
" >> $outputFile
+ count=$((count+1))
+done
+echo "
" >> $outputFile
+
+#echo "
" >> $outputFile
+
+hasReplicateColumn="$(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 "
" >> $outputFile
+ for sample in $samples; do
+ clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)"
+ echo "
" >> $outputFile
+ echo "Clonality Score: $clonalityScore |
" >> $outputFile
+
+ #replicate,reads,squared
+ echo "Replicate ID | Number of Reads | Reads Squared | |
" >> $outputFile
+ while IFS=, read replicate reads squared
+ do
+
+ echo "$replicate | $reads | $squared | |
" >> $outputFile
+ done < $outputDir/ReplicateReads_$sample.csv
+
+ #sum of reads and reads squared
+ while IFS=, read readsSum squaredSum
+ do
+ echo "Sum | $readsSum | $squaredSum |
" >> $outputFile
+ done < $outputDir/ReplicateSumReads_$sample.csv
+
+ #overview
+ echo "Coincidence Type | Raw Coincidence Freq | Coincidence Weight | Coincidences, Weighted |
" >> $outputFile
+ while IFS=, read type count weight weightedCount
+ do
+ echo "$type | $count | $weight | $weightedCount |
" >> $outputFile
+ done < $outputDir/ClonalityOverView_$sample.csv
+ echo "
" >> $outputFile
+ done
+ echo "
" >> $outputFile
+fi
+
+hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)"
+
+if [[ "$hasJunctionData" == "Yes" ]] ; then
+ echo "
Sample | unique | VH.DEL | P1 | N1 | P2 | DEL.DH | DH.DEL | P3 | N2 | P4 | DEL.JH | Total.Del | Total.N | Total.P |
" >> $outputFile
+ while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP
+ do
+ echo "$Sample | $unique | $VHDEL | $P1 | $N1 | $P2 | $DELDH | $DHDEL | $P3 | $N2 | $P4 | $DELJH | $TotalDel | $TotalN | $TotalP |
" >> $outputFile
+ done < $outputDir/junctionAnalysis.csv
+ echo "
" >> $outputFile
+fi
+
+echo "
" >> $outputFile
+echo "
" >> $outputFile
+echo "
" >> $outputFile
+echo "
" >> $outputFile
+echo "
" >> $outputFile
+
+echo "
" >> $outputFile
+echo "
" >> $outputFile
+echo "Description | Link |
" >> $outputFile
+echo "The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
+echo "The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
+
+echo "The dataset used to generate the CDR3 length frequency graph | Download |
" >> $outputFile
+
+echo "The dataset used to generate the V gene family frequency graph | Download |
" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+ echo "The dataset used to generate the D gene family frequency graph | Download |
" >> $outputFile
+fi
+echo "The dataset used to generate the J gene family frequency graph | Download |
" >> $outputFile
+
+echo "The dataset used to generate the V gene frequency graph | Download |
" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+ echo "The dataset used to generate the D gene frequency graph | Download |
" >> $outputFile
+fi
+echo "The dataset used to generate the J gene frequency graph | Download |
" >> $outputFile
+
+for sample in $samples; do
+ if [[ "$useD" == "true" ]] ; then
+ echo "The data used to generate the VD heatmap for $sample. | Download |
" >> $outputFile
+ fi
+ echo "The data used to generate the VJ heatmap for $sample. | Download |
" >> $outputFile
+ if [[ "$useD" == "true" ]] ; then
+ echo "The data used to generate the DJ heatmap for $sample. | Download |
" >> $outputFile
+ fi
+done
+
+echo "
" >> $outputFile
+echo "
" >> $outputFile
diff -r 8b46fca04595 -r a9053212a462 r_wrapper_b.sh
--- 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 "
Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)" > $2
-echo "
Report on:" >> $outputFile
-for sample in $samples; do
- echo " $sample" >> $outputFile
-done
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-
-echo "

" >> $outputFile
-echo "

" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "

" >> $outputFile
-fi
-echo "

" >> $outputFile
-echo "

" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "

" >> $outputFile
-fi
-echo "

" >> $outputFile
-
-count=1
-echo "
" >> $outputFile
-for sample in $samples; do
- echo "
" >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo " | " >> $outputFile
- fi
- echo " | " >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo " | " >> $outputFile
- fi
- echo "
" >> $outputFile
- count=$((count+1))
-done
-echo "
" >> $outputFile
-
-#echo "
" >> $outputFile
-
-hasReplicateColumn="$(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 "
" >> $outputFile
- for sample in $samples; do
- clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)"
- echo "
" >> $outputFile
- echo "Clonality Score: $clonalityScore |
" >> $outputFile
-
- #replicate,reads,squared
- echo "Replicate ID | Number of Reads | Reads Squared | |
" >> $outputFile
- while IFS=, read replicate reads squared
- do
-
- echo "$replicate | $reads | $squared | |
" >> $outputFile
- done < $outputDir/ReplicateReads_$sample.csv
-
- #sum of reads and reads squared
- while IFS=, read readsSum squaredSum
- do
- echo "Sum | $readsSum | $squaredSum |
" >> $outputFile
- done < $outputDir/ReplicateSumReads_$sample.csv
-
- #overview
- echo "Coincidence Type | Raw Coincidence Freq | Coincidence Weight | Coincidences, Weighted |
" >> $outputFile
- while IFS=, read type count weight weightedCount
- do
- echo "$type | $count | $weight | $weightedCount |
" >> $outputFile
- done < $outputDir/ClonalityOverView_$sample.csv
- echo "
" >> $outputFile
- done
- echo "
" >> $outputFile
-fi
-
-hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)"
-
-if [[ "$hasJunctionData" == "Yes" ]] ; then
- echo "
Sample | unique | VH.DEL | P1 | N1 | P2 | DEL.DH | DH.DEL | P3 | N2 | P4 | DEL.JH | Total.Del | Total.N | Total.P |
" >> $outputFile
- while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP
- do
- echo "$Sample | $unique | $VHDEL | $P1 | $N1 | $P2 | $DELDH | $DHDEL | $P3 | $N2 | $P4 | $DELJH | $TotalDel | $TotalN | $TotalP |
" >> $outputFile
- done < $outputDir/junctionAnalysis.csv
- echo "
" >> $outputFile
-fi
-
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "Description | Link |
" >> $outputFile
-echo "The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
-echo "The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
-
-echo "The dataset used to generate the CDR3 length frequency graph | Download |
" >> $outputFile
-
-echo "The dataset used to generate the V gene family frequency graph | Download |
" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "The dataset used to generate the D gene family frequency graph | Download |
" >> $outputFile
-fi
-echo "The dataset used to generate the J gene family frequency graph | Download |
" >> $outputFile
-
-echo "The dataset used to generate the V gene frequency graph | Download |
" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "The dataset used to generate the D gene frequency graph | Download |
" >> $outputFile
-fi
-echo "The dataset used to generate the J gene frequency graph | Download |
" >> $outputFile
-
-for sample in $samples; do
- if [[ "$useD" == "true" ]] ; then
- echo "The data used to generate the VD heatmap for $sample. | Download |
" >> $outputFile
- fi
- echo "The data used to generate the VJ heatmap for $sample. | Download |
" >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo "The data used to generate the DJ heatmap for $sample. | Download |
" >> $outputFile
- fi
-done
-
-echo "
" >> $outputFile
-echo "
" >> $outputFile
diff -r 8b46fca04595 -r a9053212a462 r_wrapper_t.sh
--- 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 "
Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)" > $2
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-
-echo "

" >> $outputFile
-echo "

" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "

" >> $outputFile
-fi
-echo "

" >> $outputFile
-echo "

" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "

" >> $outputFile
-fi
-echo "

" >> $outputFile
-
-samples=`cat $outputDir/samples.txt`
-count=1
-echo "
" >> $outputFile
-for sample in $samples; do
- echo "
" >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo " | " >> $outputFile
- fi
- echo " | " >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo " | " >> $outputFile
- fi
- echo "
" >> $outputFile
- count=$((count+1))
-done
-echo "
" >> $outputFile
-
-
-hasReplicateColumn="$(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 "
" >> $outputFile
- for sample in $samples; do
- clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)"
- echo "
" >> $outputFile
- echo "Clonality Score: $clonalityScore |
" >> $outputFile
-
- #replicate,reads,squared
- echo "Replicate ID | Number of Reads | Reads Squared | |
" >> $outputFile
- while IFS=, read replicate reads squared
- do
-
- echo "$replicate | $reads | $squared | |
" >> $outputFile
- done < $outputDir/ReplicateReads_$sample.csv
-
- #sum of reads and reads squared
- while IFS=, read readsSum squaredSum
- do
- echo "Sum | $readsSum | $squaredSum |
" >> $outputFile
- done < $outputDir/ReplicateSumReads_$sample.csv
-
- #overview
- echo "Coincidence Type | Raw Coincidence Freq | Coincidence Weight | Coincidences, Weighted |
" >> $outputFile
- while IFS=, read type count weight weightedCount
- do
- echo "$type | $count | $weight | $weightedCount |
" >> $outputFile
- done < $outputDir/ClonalityOverView_$sample.csv
- echo "
" >> $outputFile
- done
- echo "
" >> $outputFile
-fi
-
-hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt nb'; then echo 'Yes'; else echo 'No'; fi)"
-
-if [[ "$hasJunctionData" == "Yes" ]] ; then
- echo "
Sample | unique | VH.DEL | P1 | N1 | P2 | DEL.DH | DH.DEL | P3 | N2 | P4 | DEL.JH | Total.Del | Total.N | Total.P |
" >> $outputFile
- while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP
- do
- echo "$Sample | $unique | $VHDEL | $P1 | $N1 | $P2 | $DELDH | $DHDEL | $P3 | $N2 | $P4 | $DELJH | $TotalDel | $TotalN | $TotalP |
" >> $outputFile
- done < $outputDir/junctionAnalysis.csv
- echo "
" >> $outputFile
-fi
-
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-
-echo "
" >> $outputFile
-echo "
" >> $outputFile
-echo "Description | Link |
" >> $outputFile
-echo "The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
-echo "The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType) | Download |
" >> $outputFile
-
-echo "The dataset used to generate the CDR3 length frequency graph | Download |
" >> $outputFile
-
-echo "The dataset used to generate the V gene family frequency graph | Download |
" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "The dataset used to generate the D gene family frequency graph | Download |
" >> $outputFile
-fi
-echo "The dataset used to generate the J gene family frequency graph | Download |
" >> $outputFile
-
-echo "The dataset used to generate the V gene frequency graph | Download |
" >> $outputFile
-if [[ "$useD" == "true" ]] ; then
- echo "The dataset used to generate the D gene frequency graph | Download |
" >> $outputFile
-fi
-echo "The dataset used to generate the J gene frequency graph | Download |
" >> $outputFile
-
-for sample in $samples; do
- if [[ "$useD" == "true" ]] ; then
- echo "The data used to generate the VD heatmap for $sample. | Download |
" >> $outputFile
- fi
- echo "The data used to generate the VJ heatmap for $sample. | Download |
" >> $outputFile
- if [[ "$useD" == "true" ]] ; then
- echo "The data used to generate the DJ heatmap for $sample. | Download |
" >> $outputFile
- fi
-done
-
-echo "
" >> $outputFile
-echo "