Mercurial > repos > davidvanzessen > prisca
changeset 0:ed6885c85660 draft
Uploaded
| author | davidvanzessen |
|---|---|
| date | Wed, 31 Aug 2016 05:31:47 -0400 |
| parents | |
| children | 75853bceec00 |
| files | ALL.xml RScript.r jquery-1.11.0.min.js script.js style.css tabber.js wrapper.sh |
| diffstat | 7 files changed, 1750 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ALL.xml Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,46 @@ +<tool id="prisca" name="PRISCA" version="1.0"> + <description>PRecISe Clonal Analysis</description> + <command interpreter="bash"> + wrapper.sh $in_file $out_file $out_file.files_path $min_freq $min_cells $merge_on + </command> + <inputs> + <param name="in_file" format="tabular" type="data" label="Data to Process" /> + <param name="min_freq" type="text" label="Minimum Frequency, between 0 and 100 in percentage" value='0'/> + <param name="min_cells" type="text" label="Minimum cell count" value='0'/> + <param name="merge_on" type="select" label="Merge On"> + <option value="Clone_Sequence">Clone_Sequence</option> + <option value="V_J_CDR3">V+J+CDR3</option> + </param> + </inputs> + <outputs> + <data format="html" name="out_file" /> + </outputs> + <help> +Takes a tabular file as input, it needs to following columns: + ++----------------------------------+----------------------------------------------+ +| **Column name** | **Column contents** | ++----------------------------------+----------------------------------------------+ +| Patient | The patient ID | ++----------------------------------+----------------------------------------------+ +| Sample | The Sample ID, one, two or three per Patient | ++----------------------------------+----------------------------------------------+ +| Cell_Count | The cell count within a sample/loci | ++----------------------------------+----------------------------------------------+ +| Clone_Molocule_Count_From_Spikes | The count of a clone | ++----------------------------------+----------------------------------------------+ +| Log10_Frequency | The frequency of a clone in log10 | ++----------------------------------+----------------------------------------------+ +| J_Segment_Major_Gene | The J Gene of this clone | ++----------------------------------+----------------------------------------------+ +| V_Segment_Major_Gene | The V Gene of this clone | ++----------------------------------+----------------------------------------------+ +| Clone_Sequence | The entire sequence | ++----------------------------------+----------------------------------------------+ +| CDR3_Sense_Sequence | The CDR3 sequence region. | ++----------------------------------+----------------------------------------------+ + + +And generate a detailed HTML report on the sequences found in indiviual samples and in both samples. + </help> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RScript.r Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,1076 @@ +args <- commandArgs(trailingOnly = TRUE) +options(scipen=999) + +inFile = args[1] +outDir = args[2] +logfile = args[3] +min_freq = as.numeric(args[4]) +min_cells = as.numeric(args[5]) +mergeOn = args[6] + +cat("<html><table><tr><td>Starting analysis</td></tr>", file=logfile, append=F) + +library(ggplot2) +library(reshape2) +library(data.table) +library(grid) +library(parallel) +#require(xtable) +cat("<tr><td>Reading input</td></tr>", file=logfile, append=T) +dat = read.table(inFile, header=T, sep="\t", dec=".", fill=T, stringsAsFactors=F) +dat = dat[,c("Patient", "Receptor", "Sample", "Cell_Count", "Clone_Molecule_Count_From_Spikes", "Log10_Frequency", "Total_Read_Count", "J_Segment_Major_Gene", "V_Segment_Major_Gene", "CDR3_Sense_Sequence", "Clone_Sequence")] +dat$dsPerM = 0 +dat = dat[!is.na(dat$Patient),] +dat$Related_to_leukemia_clone = F + +setwd(outDir) +cat("<tr><td>Selecting first V/J Genes</td></tr>", file=logfile, append=T) +dat$V_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$V_Segment_Major_Gene), "; "), "[[", 1))) +dat$J_Segment_Major_Gene = as.factor(as.character(lapply(strsplit(as.character(dat$J_Segment_Major_Gene), "; "), "[[", 1))) + +cat("<tr><td>Calculating Frequency</td></tr>", file=logfile, append=T) + +dat$Frequency = ((10^dat$Log10_Frequency)*100) + +dat = dat[dat$Frequency >= min_freq,] + +triplets = dat[grepl("VanDongen_cALL_14696", dat$Patient) | grepl("(16278)|(26402)|(26759)", dat$Sample),] + +cat("<tr><td>Normalizing to lowest cell count within locus</td></tr>", file=logfile, append=T) + +dat$locus_V = substring(dat$V_Segment_Major_Gene, 0, 4) +dat$locus_J = substring(dat$J_Segment_Major_Gene, 0, 4) +min_cell_count = data.frame(data.table(dat)[, list(min_cell_count=min(.SD$Cell_Count)), by=c("Patient", "locus_V", "locus_J")]) + +dat$min_cell_paste = paste(dat$Patient, dat$locus_V, dat$locus_J) +min_cell_count$min_cell_paste = paste(min_cell_count$Patient, min_cell_count$locus_V, min_cell_count$locus_J) + +min_cell_count = min_cell_count[,c("min_cell_paste", "min_cell_count")] +print(paste("rows:", nrow(dat))) +dat = merge(dat, min_cell_count, by="min_cell_paste") +print(paste("rows:", nrow(dat))) +dat$normalized_read_count = round(dat$Clone_Molecule_Count_From_Spikes / dat$Cell_Count * dat$min_cell_count / 2, digits=2) #??????????????????????????????????? wel of geen / 2 + +dat = dat[dat$normalized_read_count >= min_cells,] + +dat$paste = paste(dat$Sample, dat$Clone_Sequence) + +patients = split(dat, dat$Patient, drop=T) +intervalReads = rev(c(0,10,25,50,100,250,500,750,1000,10000)) +intervalFreq = rev(c(0,0.01,0.05,0.1,0.5,1,5)) +V_Segments = c(".*", "IGHV", "IGHD", "IGKV", "IGKV", "IgKINTR", "TRGV", "TRDV", "TRDD" , "TRBV") +J_Segments = c(".*", ".*", ".*", "IGKJ", "KDE", ".*", ".*", ".*", ".*", ".*") +Titles = c("Total", "IGH-Vh-Jh", "IGH-Dh-Jh", "Vk-Jk", "Vk-Kde" , "Intron-Kde", "TCRG", "TCRD-Vd-Dd", "TCRD-Dd-Dd", "TCRB-Vb-Jb") +Titles = factor(Titles, levels=Titles) +TitlesOrder = data.frame("Title"=Titles, "TitlesOrder"=1:length(Titles)) + +single_patients = data.frame("Patient" = character(0),"Sample" = character(0), "on" = character(0), "Clone_Sequence" = character(0), "Frequency" = numeric(0), "normalized_read_count" = numeric(0), "V_Segment_Major_Gene" = character(0), "J_Segment_Major_Gene" = character(0), "Rearrangement" = character(0)) + +patient.merge.list = list() #cache the 'both' table, 2x speedup for more memory... +patient.merge.list.second = list() + scatter_locus_data_list = list() +cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="multiple_matches.html", append=T) +cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="single_matches.html", append=T) +patientCountOnColumn <- function(x, product, interval, on, appendtxt=F){ + if (!is.data.frame(x) & is.list(x)){ + x = x[[1]] + } + #x$Sample = factor(x$Sample, levels=unique(x$Sample)) + x = data.frame(x,stringsAsFactors=F) + onShort = "reads" + if(on == "Frequency"){ + onShort = "freq" + } + onx = paste(on, ".x", sep="") + ony = paste(on, ".y", sep="") + splt = split(x, x$Sample, drop=T) + type="pair" + if(length(splt) == 1){ + print(paste(paste(x[1,which(colnames(x) == "Patient")]), "has one sample")) + splt[[2]] = data.frame("Patient" = character(0), "Receptor" = character(0), "Sample" = character(0), "Cell_Count" = numeric(0), "Clone_Molecule_Count_From_Spikes" = numeric(0), "Log10_Frequency" = numeric(0), "Total_Read_Count" = numeric(0), "dsMol_per_1e6_cells" = numeric(0), "J_Segment_Major_Gene" = character(0), "V_Segment_Major_Gene" = character(0), "Clone_Sequence" = character(0), "CDR3_Sense_Sequence" = character(0), "Related_to_leukemia_clone" = logical(0), "Frequency"= numeric(0), "normalized_read_count" = numeric(0), "paste" = character(0)) + type="single" + } + patient1 = splt[[1]] + patient2 = splt[[2]] + + threshholdIndex = which(colnames(product) == "interval") + V_SegmentIndex = which(colnames(product) == "V_Segments") + J_SegmentIndex = which(colnames(product) == "J_Segments") + titleIndex = which(colnames(product) == "Titles") + sampleIndex = which(colnames(x) == "Sample") + patientIndex = which(colnames(x) == "Patient") + oneSample = paste(patient1[1,sampleIndex], sep="") + twoSample = paste(patient2[1,sampleIndex], sep="") + patient = paste(x[1,patientIndex]) + + switched = F + if(length(grep(".*_Right$", twoSample)) == 1 || length(grep(".*_Dx_BM$", twoSample)) == 1 || length(grep(".*_Dx$", twoSample)) == 1 ){ + tmp = twoSample + twoSample = oneSample + oneSample = tmp + tmp = patient1 + patient1 = patient2 + patient2 = tmp + switched = T + } + if(appendtxt){ + cat(paste(patient, oneSample, twoSample, type, sep="\t"), file="patients.txt", append=T, sep="", fill=3) + } + cat(paste("<tr><td>", patient, "</td>", sep=""), file=logfile, append=T) + + if(mergeOn == "Clone_Sequence"){ + patient1$merge = paste(patient1$Clone_Sequence) + patient2$merge = paste(patient2$Clone_Sequence) + } else { + patient1$merge = paste(patient1$V_Segment_Major_Gene, patient1$J_Segment_Major_Gene, patient1$CDR3_Sense_Sequence) + patient2$merge = paste(patient2$V_Segment_Major_Gene, patient2$J_Segment_Major_Gene, patient2$CDR3_Sense_Sequence) + } + + scatterplot_data_columns = c("Patient", "Sample", "Frequency", "normalized_read_count", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "merge") + #scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns]) + scatterplot_data = patient1[NULL,scatterplot_data_columns] + #scatterplot_data = scatterplot_data[!duplicated(scatterplot_data$merge),] + #scatterplot_data$type = factor(x=oneSample, levels=c(oneSample, twoSample, "In Both")) + scatterplot.data.type.factor = c(oneSample, twoSample, paste(c(oneSample, twoSample), "In Both")) + #scatterplot_data$type = factor(x=NULL, levels=scatterplot.data.type.factor) + scatterplot_data$type = character(0) + scatterplot_data$link = numeric(0) + scatterplot_data$on = character(0) + + #patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge") #merge alles 'fuzzy' + patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge")[NULL,] #blegh + + cs.exact.matches = patient1[patient1$Clone_Sequence %in% patient2$Clone_Sequence,]$Clone_Sequence + + start.time = proc.time() + merge.list = c() + + if(patient %in% names(patient.merge.list)){ + patientMerge = patient.merge.list[[patient]] + merge.list[["second"]] = patient.merge.list.second[[patient]] + scatterplot_data = scatter_locus_data_list[[patient]] + cat(paste("<td>", nrow(patient1), " in ", oneSample, " and ", nrow(patient2), " in ", twoSample, ", ", nrow(patientMerge), " in both (fetched from cache)</td></tr>", sep=""), file=logfile, append=T) + + print(names(patient.merge.list)) + } else { + #fuzzy matching here... + #merge.list = patientMerge$merge + + #patient1.fuzzy = patient1[!(patient1$merge %in% merge.list),] + #patient2.fuzzy = patient2[!(patient2$merge %in% merge.list),] + + patient1.fuzzy = patient1 + patient2.fuzzy = patient2 + + #patient1.fuzzy$merge = paste(patient1.fuzzy$V_Segment_Major_Gene, patient1.fuzzy$J_Segment_Major_Gene, patient1.fuzzy$CDR3_Sense_Sequence) + #patient2.fuzzy$merge = paste(patient2.fuzzy$V_Segment_Major_Gene, patient2.fuzzy$J_Segment_Major_Gene, patient2.fuzzy$CDR3_Sense_Sequence) + + #patient1.fuzzy$merge = paste(patient1.fuzzy$locus_V, patient1.fuzzy$locus_J, patient1.fuzzy$CDR3_Sense_Sequence) + #patient2.fuzzy$merge = paste(patient2.fuzzy$locus_V, patient2.fuzzy$locus_J, patient2.fuzzy$CDR3_Sense_Sequence) + + patient1.fuzzy$merge = paste(patient1.fuzzy$locus_V, patient1.fuzzy$locus_J) + patient2.fuzzy$merge = paste(patient2.fuzzy$locus_V, patient2.fuzzy$locus_J) + + #merge.freq.table = data.frame(table(c(patient1.fuzzy[!duplicated(patient1.fuzzy$merge),"merge"], patient2.fuzzy[!duplicated(patient2.fuzzy$merge),"merge"]))) #also remove? + #merge.freq.table.gt.1 = merge.freq.table[merge.freq.table$Freq > 1,] + + #patient1.fuzzy = patient1.fuzzy[patient1.fuzzy$merge %in% merge.freq.table.gt.1$Var1,] + #patient2.fuzzy = patient2.fuzzy[patient2.fuzzy$merge %in% merge.freq.table.gt.1$Var1,] + + patient.fuzzy = rbind(patient1.fuzzy, patient2.fuzzy) + patient.fuzzy = patient.fuzzy[order(nchar(patient.fuzzy$Clone_Sequence)),] + + merge.list = list() + + merge.list[["second"]] = vector() + + link.count = 1 + + while(nrow(patient.fuzzy) > 1){ + first.merge = patient.fuzzy[1,"merge"] + first.clone.sequence = patient.fuzzy[1,"Clone_Sequence"] + first.sample = patient.fuzzy[1,"Sample"] + merge.filter = first.merge == patient.fuzzy$merge + + #length.filter = nchar(patient.fuzzy$Clone_Sequence) - nchar(first.clone.sequence) <= 9 + + first.sample.filter = first.sample == patient.fuzzy$Sample + second.sample.filter = first.sample != patient.fuzzy$Sample + + #first match same sample, sum to a single row, same for other sample + #then merge rows like 'normal' + + sequence.filter = grepl(paste("^", first.clone.sequence, sep=""), patient.fuzzy$Clone_Sequence) + + + + #match.filter = merge.filter & grepl(first.clone.sequence, patient.fuzzy$Clone_Sequence) & length.filter & sample.filter + first.match.filter = merge.filter & sequence.filter & first.sample.filter + second.match.filter = merge.filter & sequence.filter & second.sample.filter + + first.rows = patient.fuzzy[first.match.filter,] + second.rows = patient.fuzzy[second.match.filter,] + + first.rows.v = table(first.rows$V_Segment_Major_Gene) + first.rows.v = names(first.rows.v[which.max(first.rows.v)]) + first.rows.j = table(first.rows$J_Segment_Major_Gene) + first.rows.j = names(first.rows.j[which.max(first.rows.j)]) + + first.sum = data.frame(merge = first.clone.sequence, + Patient = patient, + Receptor = first.rows[1,"Receptor"], + Sample = first.rows[1,"Sample"], + Cell_Count = first.rows[1,"Cell_Count"], + Clone_Molecule_Count_From_Spikes = sum(first.rows$Clone_Molecule_Count_From_Spikes), + Log10_Frequency = log10(sum(first.rows$Frequency)), + Total_Read_Count = sum(first.rows$Total_Read_Count), + dsPerM = sum(first.rows$dsPerM), + J_Segment_Major_Gene = first.rows.j, + V_Segment_Major_Gene = first.rows.v, + Clone_Sequence = first.clone.sequence, + CDR3_Sense_Sequence = first.rows[1,"CDR3_Sense_Sequence"], + Related_to_leukemia_clone = F, + Frequency = sum(first.rows$Frequency), + locus_V = first.rows[1,"locus_V"], + locus_J = first.rows[1,"locus_J"], + min_cell_count = first.rows[1,"min_cell_count"], + normalized_read_count = sum(first.rows$normalized_read_count), + paste = first.rows[1,"paste"], + min_cell_paste = first.rows[1,"min_cell_paste"]) + + if(nrow(second.rows) > 0){ + second.rows.v = table(second.rows$V_Segment_Major_Gene) + second.rows.v = names(second.rows.v[which.max(second.rows.v)]) + second.rows.j = table(second.rows$J_Segment_Major_Gene) + second.rows.j = names(second.rows.j[which.max(second.rows.j)]) + + second.sum = data.frame(merge = first.clone.sequence, + Patient = patient, + Receptor = second.rows[1,"Receptor"], + Sample = second.rows[1,"Sample"], + Cell_Count = second.rows[1,"Cell_Count"], + Clone_Molecule_Count_From_Spikes = sum(second.rows$Clone_Molecule_Count_From_Spikes), + Log10_Frequency = log10(sum(second.rows$Frequency)), + Total_Read_Count = sum(second.rows$Total_Read_Count), + dsPerM = sum(second.rows$dsPerM), + J_Segment_Major_Gene = second.rows.j, + V_Segment_Major_Gene = second.rows.v, + Clone_Sequence = first.clone.sequence, + CDR3_Sense_Sequence = second.rows[1,"CDR3_Sense_Sequence"], + Related_to_leukemia_clone = F, + Frequency = sum(second.rows$Frequency), + locus_V = second.rows[1,"locus_V"], + locus_J = second.rows[1,"locus_J"], + min_cell_count = second.rows[1,"min_cell_count"], + normalized_read_count = sum(second.rows$normalized_read_count), + paste = second.rows[1,"paste"], + min_cell_paste = second.rows[1,"min_cell_paste"]) + + patientMerge = rbind(patientMerge, merge(first.sum, second.sum, by="merge")) + patient.fuzzy = patient.fuzzy[!(first.match.filter | second.match.filter),] + + hidden.clone.sequences = c(first.rows[-1,"Clone_Sequence"], second.rows[second.rows$Clone_Sequence != first.clone.sequence,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + tmp.rows = rbind(first.rows, second.rows) + tmp.rows = tmp.rows[order(nchar(tmp.rows$Clone_Sequence)),] + + + #add to the scatterplot data + scatterplot.row = first.sum[,scatterplot_data_columns] + scatterplot.row$type = paste(first.sum[,"Sample"], "In Both") + scatterplot.row$link = link.count + scatterplot.row$on = onShort + + scatterplot_data = rbind(scatterplot_data, scatterplot.row) + + scatterplot.row = second.sum[,scatterplot_data_columns] + scatterplot.row$type = paste(second.sum[,"Sample"], "In Both") + scatterplot.row$link = link.count + scatterplot.row$on = onShort + + scatterplot_data = rbind(scatterplot_data, scatterplot.row) + + #write some information about the match to a log file + if (nrow(first.rows) > 1 | nrow(second.rows) > 1) { + cat(paste("<tr><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="multiple_matches.html", append=T) + } else { + second.clone.sequence = second.rows[1,"Clone_Sequence"] + if(nchar(first.clone.sequence) != nchar(second.clone.sequence)){ + cat(paste("<tr bgcolor='#DDD'><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T) + } else { + #cat(paste("<tr><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T) + } + } + + } else if(nrow(first.rows) > 1) { + if(patient1[1,"Sample"] == first.sample){ + patient1 = patient1[!(patient1$Clone_Sequence %in% first.rows$Clone_Sequence),] + patient1 = rbind(patient1, first.sum) + } else { + patient2 = patient2[!(patient2$Clone_Sequence %in% first.rows$Clone_Sequence),] + patient2 = rbind(patient2, first.sum) + } + + hidden.clone.sequences = c(first.rows[-1,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + patient.fuzzy = patient.fuzzy[-first.match.filter,] + + #add to the scatterplot data + scatterplot.row = first.sum[,scatterplot_data_columns] + scatterplot.row$type = first.sum[,"Sample"] + scatterplot.row$link = link.count + scatterplot.row$on = onShort + + scatterplot_data = rbind(scatterplot_data, scatterplot.row) + + cat(paste("<tr bgcolor='#DDF'><td>", patient, " row ", 1:nrow(first.rows), "</td><td>", first.rows$Sample, ":</td><td>", first.rows$Clone_Sequence, "</td><td>", first.rows$normalized_read_count, "</td></tr>", sep=""), file="single_matches.html", append=T) + } else { + patient.fuzzy = patient.fuzzy[-1,] + + #add to the scatterplot data + scatterplot.row = first.sum[,scatterplot_data_columns] + scatterplot.row$type = first.sum[,"Sample"] + scatterplot.row$link = link.count + scatterplot.row$on = onShort + + scatterplot_data = rbind(scatterplot_data, scatterplot.row) + } + link.count = link.count + 1 + } + patient.merge.list[[patient]] <<- patientMerge + patient.merge.list.second[[patient]] <<- merge.list[["second"]] + + sample.order = data.frame(type = c(oneSample, twoSample, paste(c(oneSample, twoSample), "In Both")),type.order = 1:4) + scatterplot_data = merge(scatterplot_data, sample.order, by="type") + + scatter_locus_data_list[[patient]] <<- scatterplot_data + cat(paste("<td>", nrow(patient1), " in ", oneSample, " and ", nrow(patient2), " in ", twoSample, ", ", nrow(patientMerge), " in both (finding both took ", (proc.time() - start.time)[[3]], "s)</td></tr>", sep=""), file=logfile, append=T) + } + + patient1 = patient1[!(patient1$Clone_Sequence %in% patient.merge.list.second[[patient]]),] + patient2 = patient2[!(patient2$Clone_Sequence %in% patient.merge.list.second[[patient]]),] + + + patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony]) + #patientMerge$thresholdValue = pmin(patientMerge[,onx], patientMerge[,ony]) + res1 = vector() + res2 = vector() + resBoth = vector() + read1Count = vector() + read2Count = vector() + locussum1 = vector() + locussum2 = vector() + + #for(iter in 1){ + for(iter in 1:length(product[,1])){ + threshhold = product[iter,threshholdIndex] + V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="") + J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="") + #both = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge[,onx] > threshhold & patientMerge[,ony] > threshhold) #both higher than threshold + both = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge$thresholdValue > threshhold) #highest of both is higher than threshold + one = (grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene) & patient1[,on] > threshhold & !(patient1$merge %in% patientMerge[both,]$merge)) + two = (grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene) & patient2[,on] > threshhold & !(patient2$merge %in% patientMerge[both,]$merge)) + read1Count = append(read1Count, sum(patient1[one,]$normalized_read_count)) + read2Count = append(read2Count, sum(patient2[two,]$normalized_read_count)) + res1 = append(res1, sum(one)) + res2 = append(res2, sum(two)) + resBoth = append(resBoth, sum(both)) + locussum1 = append(locussum1, sum(patient1[(grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene)),]$normalized_read_count)) + locussum2 = append(locussum2, sum(patient2[(grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene)),]$normalized_read_count)) + #threshhold = 0 + if(threshhold != 0){ + if(sum(one) > 0){ + dfOne = patient1[one,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")] + colnames(dfOne) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone Sequence", "Related_to_leukemia_clone") + filenameOne = paste(oneSample, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfOne, file=paste(filenameOne, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(two) > 0){ + dfTwo = patient2[two,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")] + colnames(dfTwo) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone Sequence", "Related_to_leukemia_clone") + filenameTwo = paste(twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfTwo, file=paste(filenameTwo, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + } else { + scatterplot_locus_data = scatterplot_data[grepl(V_Segment, scatterplot_data$V_Segment_Major_Gene) & grepl(J_Segment, scatterplot_data$J_Segment_Major_Gene),] + if(nrow(scatterplot_locus_data) > 0){ + scatterplot_locus_data$Rearrangement = product[iter, titleIndex] + } + + + + p = NULL + print(paste("nrow scatterplot_locus_data", nrow(scatterplot_locus_data))) + if(nrow(scatterplot_locus_data) != 0){ + if(on == "normalized_read_count"){ + write.table(scatterplot_locus_data, file=paste(oneSample, twoSample, product[iter, titleIndex], "scatterplot_locus_data.txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count)))) + p = ggplot(scatterplot_locus_data, aes(factor(reorder(type, type.order)), normalized_read_count, group=link)) + geom_line() + scale_y_log10(breaks=scales,labels=scales, limits=c(1,1e6)) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + } else { + p = ggplot(scatterplot_locus_data, aes(factor(reorder(type, type.order)), Frequency, group=link)) + geom_line() + scale_y_log10(limits=c(0.0001,100), breaks=c(0.0001, 0.001, 0.01, 0.1, 1, 10, 100), labels=c("0.0001", "0.001", "0.01", "0.1", "1", "10", "100")) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + } + p = p + geom_point(aes(colour=type), position="dodge") + p = p + xlab("In one or both samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex])) + } else { + p = ggplot(NULL, aes(x=c("In one", "In Both"),y=0)) + geom_blank(NULL) + xlab("In one or both of the samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex])) + } + png(paste(patient1[1,patientIndex], "_", patient1[1,sampleIndex], "_", patient2[1,sampleIndex], "_", onShort, "_", product[iter, titleIndex],"_scatter.png", sep="")) + print(p) + dev.off() + } + if(sum(both) > 0){ + dfBoth = patientMerge[both,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")] + colnames(dfBoth) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample)) + filenameBoth = paste(oneSample, "_", twoSample, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfBoth, file=paste(filenameBoth, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + } + patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "Both"=resBoth, "tmp1"=res1, "read_count1" = round(read1Count), "tmp2"=res2, "read_count2"= round(read2Count), "Sum"=res1 + res2 + resBoth, "percentage" = round((resBoth/(res1 + res2 + resBoth)) * 100, digits=2), "Locus_sum1"=locussum1, "Locus_sum2"=locussum2) + if(sum(is.na(patientResult$percentage)) > 0){ + patientResult[is.na(patientResult$percentage),]$percentage = 0 + } + colnames(patientResult)[6] = oneSample + colnames(patientResult)[8] = twoSample + colnamesBak = colnames(patientResult) + colnames(patientResult) = c("Ig/TCR gene rearrangement type", "Distal Gene segment", "Proximal gene segment", "cut_off_value", paste("Number of sequences ", patient, "_Both", sep=""), paste("Number of sequences", oneSample, sep=""), paste("Normalized Read Count", oneSample), paste("Number of sequences", twoSample, sep=""), paste("Normalized Read Count", twoSample), paste("Sum number of sequences", patient), paste("Percentage of sequences ", patient, "_Both", sep=""), paste("Locus Sum", oneSample), paste("Locus Sum", twoSample)) + write.table(patientResult, file=paste(patient, "_", onShort, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + colnames(patientResult) = colnamesBak + + patientResult$Locus = factor(patientResult$Locus, Titles) + patientResult$cut_off_value = factor(patientResult$cut_off_value, paste(">", interval, sep="")) + + plt = ggplot(patientResult[,c("Locus", "cut_off_value", "Both")]) + plt = plt + geom_bar( aes( x=factor(cut_off_value), y=Both), stat='identity', position="dodge", fill="#79c36a") + plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + plt = plt + geom_text(aes(ymax=max(Both), x=cut_off_value,y=Both,label=Both), angle=90, hjust=0) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in both") + plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines")) + png(paste(patient, "_", onShort, ".png", sep=""), width=1920, height=1080) + print(plt) + dev.off() + #(t,r,b,l) + plt = ggplot(patientResult[,c("Locus", "cut_off_value", "percentage")]) + plt = plt + geom_bar( aes( x=factor(cut_off_value), y=percentage), stat='identity', position="dodge", fill="#79c36a") + plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + plt = plt + geom_text(aes(ymax=max(percentage), x=cut_off_value,y=percentage,label=percentage), angle=90, hjust=0) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("% clones in both left and right") + plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines")) + png(paste(patient, "_percent_", onShort, ".png", sep=""), width=1920, height=1080) + print(plt) + dev.off() + + patientResult = melt(patientResult[,c('Locus','cut_off_value', oneSample, twoSample)] ,id.vars=1:2) + patientResult$relativeValue = patientResult$value * 10 + patientResult[patientResult$relativeValue == 0,]$relativeValue = 1 + plt = ggplot(patientResult) + plt = plt + geom_bar( aes( x=factor(cut_off_value), y=relativeValue, fill=variable), stat='identity', position="dodge") + plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + plt = plt + scale_y_continuous(trans="log", breaks=10^c(0:10), labels=c(0, 10^c(0:9))) + plt = plt + geom_text(data=patientResult[patientResult$variable == oneSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=-0.2) + plt = plt + geom_text(data=patientResult[patientResult$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.8) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle(paste("Number of clones in only ", oneSample, " and only ", twoSample, sep="")) + png(paste(patient, "_", onShort, "_both.png", sep=""), width=1920, height=1080) + print(plt) + dev.off() +} + +cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T) + +interval = intervalFreq +intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) +product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) +lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T) + +cat("<tr><td>Starting Cell Count analysis</td></tr>", file=logfile, append=T) + +interval = intervalReads +intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) +product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) +lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="normalized_read_count") + +if(nrow(single_patients) > 0){ + scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count)))) + p = ggplot(single_patients, aes(Rearrangement, normalized_read_count)) + scale_y_log10(breaks=scales,labels=as.character(scales)) + expand_limits(y=c(0,1000000)) + p = p + geom_point(aes(colour=type), position="jitter") + p = p + xlab("In one or both samples") + ylab("Reads") + p = p + facet_grid(.~Patient) + ggtitle("Scatterplot of the reads of the patients with a single sample") + png("singles_reads_scatterplot.png", width=640 * length(unique(single_patients$Patient)) + 100, height=1080) + print(p) + dev.off() + + #p = ggplot(single_patients, aes(Rearrangement, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + p = ggplot(single_patients, aes(Rearrangement, Frequency)) + scale_y_log10(limits=c(0.0001,100), breaks=c(0.0001, 0.001, 0.01, 0.1, 1, 10, 100), labels=c("0.0001", "0.001", "0.01", "0.1", "1", "10", "100")) + expand_limits(y=c(0,100)) + p = p + geom_point(aes(colour=type), position="jitter") + p = p + xlab("In one or both samples") + ylab("Frequency") + p = p + facet_grid(.~Patient) + ggtitle("Scatterplot of the frequency of the patients with a single sample") + png("singles_freq_scatterplot.png", width=640 * length(unique(single_patients$Patient)) + 100, height=1080) + print(p) + dev.off() +} else { + empty <- data.frame() + p = ggplot(empty) + geom_point() + xlim(0, 10) + ylim(0, 100) + xlab("In one or both samples") + ylab("Frequency") + ggtitle("Scatterplot of the frequency of the patients with a single sample") + + png("singles_reads_scatterplot.png", width=400, height=300) + print(p) + dev.off() + + png("singles_freq_scatterplot.png", width=400, height=300) + print(p) + dev.off() +} + +patient.merge.list = list() #cache the 'both' table, 2x speedup for more memory... +patient.merge.list.second = list() + +tripletAnalysis <- function(patient1, label1, patient2, label2, patient3, label3, product, interval, on, appendTriplets= FALSE){ + onShort = "reads" + if(on == "Frequency"){ + onShort = "freq" + } + onx = paste(on, ".x", sep="") + ony = paste(on, ".y", sep="") + onz = paste(on, ".z", sep="") + type="triplet" + + threshholdIndex = which(colnames(product) == "interval") + V_SegmentIndex = which(colnames(product) == "V_Segments") + J_SegmentIndex = which(colnames(product) == "J_Segments") + titleIndex = which(colnames(product) == "Titles") + sampleIndex = which(colnames(patient1) == "Sample") + patientIndex = which(colnames(patient1) == "Patient") + oneSample = paste(patient1[1,sampleIndex], sep="") + twoSample = paste(patient2[1,sampleIndex], sep="") + threeSample = paste(patient3[1,sampleIndex], sep="") + + if(mergeOn == "Clone_Sequence"){ + patient1$merge = paste(patient1$Clone_Sequence) + patient2$merge = paste(patient2$Clone_Sequence) + patient3$merge = paste(patient3$Clone_Sequence) + + } else { + patient1$merge = paste(patient1$V_Segment_Major_Gene, patient1$J_Segment_Major_Gene, patient1$CDR3_Sense_Sequence) + patient2$merge = paste(patient2$V_Segment_Major_Gene, patient2$J_Segment_Major_Gene, patient2$CDR3_Sense_Sequence) + patient3$merge = paste(patient3$V_Segment_Major_Gene, patient3$J_Segment_Major_Gene, patient3$CDR3_Sense_Sequence) + } + + #patientMerge = merge(patient1, patient2, by="merge")[NULL,] + patient1.fuzzy = patient1 + patient2.fuzzy = patient2 + patient3.fuzzy = patient3 + + cat(paste("<tr><td>", label1, "</td>", sep=""), file=logfile, append=T) + + patient1.fuzzy$merge = paste(patient1.fuzzy$locus_V, patient1.fuzzy$locus_J) + patient2.fuzzy$merge = paste(patient2.fuzzy$locus_V, patient2.fuzzy$locus_J) + patient3.fuzzy$merge = paste(patient3.fuzzy$locus_V, patient3.fuzzy$locus_J) + + patient.fuzzy = rbind(patient1.fuzzy ,patient2.fuzzy, patient3.fuzzy) + patient.fuzzy = patient.fuzzy[order(nchar(patient.fuzzy$Clone_Sequence)),] + + other.sample.list = list() + other.sample.list[[oneSample]] = c(twoSample, threeSample) + other.sample.list[[twoSample]] = c(oneSample, threeSample) + other.sample.list[[threeSample]] = c(oneSample, twoSample) + + patientMerge = merge(patient1, patient2, by="merge") + patientMerge = merge(patientMerge, patient3, by="merge") + colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge)))] = paste(colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge), perl=T))], ".z", sep="") + #patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + patientMerge = patientMerge[NULL,] + + duo.merge.list = list() + + patientMerge12 = merge(patient1, patient2, by="merge") + #patientMerge12$thresholdValue = pmax(patientMerge12[,onx], patientMerge12[,ony]) + patientMerge12 = patientMerge12[NULL,] + duo.merge.list[[paste(oneSample, twoSample)]] = patientMerge12 + duo.merge.list[[paste(twoSample, oneSample)]] = patientMerge12 + + patientMerge13 = merge(patient1, patient3, by="merge") + #patientMerge13$thresholdValue = pmax(patientMerge13[,onx], patientMerge13[,ony]) + patientMerge13 = patientMerge13[NULL,] + duo.merge.list[[paste(oneSample, threeSample)]] = patientMerge13 + duo.merge.list[[paste(threeSample, oneSample)]] = patientMerge13 + + patientMerge23 = merge(patient2, patient3, by="merge") + #patientMerge23$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony]) + patientMerge23 = patientMerge23[NULL,] + duo.merge.list[[paste(twoSample, threeSample)]] = patientMerge23 + duo.merge.list[[paste(threeSample, twoSample)]] = patientMerge23 + + merge.list = list() + merge.list[["second"]] = vector() + + start.time = proc.time() + if(paste(label1, "123") %in% names(patient.merge.list)){ + patientMerge = patient.merge.list[[paste(label1, "123")]] + patientMerge12 = patient.merge.list[[paste(label1, "12")]] + patientMerge13 = patient.merge.list[[paste(label1, "13")]] + patientMerge23 = patient.merge.list[[paste(label1, "23")]] + + merge.list[["second"]] = patient.merge.list.second[[label1]] + + cat(paste("<td>", nrow(patient1), " in ", label1, " and ", nrow(patient2), " in ", label2, nrow(patient3), " in ", label3, ", ", nrow(patientMerge), " in both (fetched from cache)</td></tr>", sep=""), file=logfile, append=T) + } else { + while(nrow(patient.fuzzy) > 0){ + first.merge = patient.fuzzy[1,"merge"] + first.clone.sequence = patient.fuzzy[1,"Clone_Sequence"] + first.sample = patient.fuzzy[1,"Sample"] + + merge.filter = first.merge == patient.fuzzy$merge + + second.sample = other.sample.list[[first.sample]][1] + third.sample = other.sample.list[[first.sample]][2] + + sample.filter.1 = first.sample == patient.fuzzy$Sample + sample.filter.2 = second.sample == patient.fuzzy$Sample + sample.filter.3 = third.sample == patient.fuzzy$Sample + + sequence.filter = grepl(paste("^", first.clone.sequence, sep=""), patient.fuzzy$Clone_Sequence) + + match.filter.1 = sample.filter.1 & sequence.filter & merge.filter + match.filter.2 = sample.filter.2 & sequence.filter & merge.filter + match.filter.3 = sample.filter.3 & sequence.filter & merge.filter + + matches.in.1 = any(match.filter.1) + matches.in.2 = any(match.filter.2) + matches.in.3 = any(match.filter.3) + + + + rows.1 = patient.fuzzy[match.filter.1,] + + sum.1 = data.frame(merge = first.clone.sequence, + Patient = label1, + Receptor = rows.1[1,"Receptor"], + Sample = rows.1[1,"Sample"], + Cell_Count = rows.1[1,"Cell_Count"], + Clone_Molecule_Count_From_Spikes = sum(rows.1$Clone_Molecule_Count_From_Spikes), + Log10_Frequency = log10(sum(rows.1$Frequency)), + Total_Read_Count = sum(rows.1$Total_Read_Count), + dsPerM = sum(rows.1$dsPerM), + J_Segment_Major_Gene = rows.1[1,"J_Segment_Major_Gene"], + V_Segment_Major_Gene = rows.1[1,"V_Segment_Major_Gene"], + Clone_Sequence = first.clone.sequence, + CDR3_Sense_Sequence = rows.1[1,"CDR3_Sense_Sequence"], + Related_to_leukemia_clone = F, + Frequency = sum(rows.1$Frequency), + locus_V = rows.1[1,"locus_V"], + locus_J = rows.1[1,"locus_J"], + uniqueID = rows.1[1,"uniqueID"], + normalized_read_count = sum(rows.1$normalized_read_count)) + sum.2 = sum.1[NULL,] + rows.2 = patient.fuzzy[match.filter.2,] + if(matches.in.2){ + sum.2 = data.frame(merge = first.clone.sequence, + Patient = label1, + Receptor = rows.2[1,"Receptor"], + Sample = rows.2[1,"Sample"], + Cell_Count = rows.2[1,"Cell_Count"], + Clone_Molecule_Count_From_Spikes = sum(rows.2$Clone_Molecule_Count_From_Spikes), + Log10_Frequency = log10(sum(rows.2$Frequency)), + Total_Read_Count = sum(rows.2$Total_Read_Count), + dsPerM = sum(rows.2$dsPerM), + J_Segment_Major_Gene = rows.2[1,"J_Segment_Major_Gene"], + V_Segment_Major_Gene = rows.2[1,"V_Segment_Major_Gene"], + Clone_Sequence = first.clone.sequence, + CDR3_Sense_Sequence = rows.2[1,"CDR3_Sense_Sequence"], + Related_to_leukemia_clone = F, + Frequency = sum(rows.2$Frequency), + locus_V = rows.2[1,"locus_V"], + locus_J = rows.2[1,"locus_J"], + uniqueID = rows.2[1,"uniqueID"], + normalized_read_count = sum(rows.2$normalized_read_count)) + } + + sum.3 = sum.1[NULL,] + rows.3 = patient.fuzzy[match.filter.3,] + if(matches.in.3){ + sum.3 = data.frame(merge = first.clone.sequence, + Patient = label1, + Receptor = rows.3[1,"Receptor"], + Sample = rows.3[1,"Sample"], + Cell_Count = rows.3[1,"Cell_Count"], + Clone_Molecule_Count_From_Spikes = sum(rows.3$Clone_Molecule_Count_From_Spikes), + Log10_Frequency = log10(sum(rows.3$Frequency)), + Total_Read_Count = sum(rows.3$Total_Read_Count), + dsPerM = sum(rows.3$dsPerM), + J_Segment_Major_Gene = rows.3[1,"J_Segment_Major_Gene"], + V_Segment_Major_Gene = rows.3[1,"V_Segment_Major_Gene"], + Clone_Sequence = first.clone.sequence, + CDR3_Sense_Sequence = rows.3[1,"CDR3_Sense_Sequence"], + Related_to_leukemia_clone = F, + Frequency = sum(rows.3$Frequency), + locus_V = rows.3[1,"locus_V"], + locus_J = rows.3[1,"locus_J"], + uniqueID = rows.3[1,"uniqueID"], + normalized_read_count = sum(rows.3$normalized_read_count)) + } + + if(matches.in.2 & matches.in.3){ + merge.123 = merge(sum.1, sum.2, by="merge") + merge.123 = merge(merge.123, sum.3, by="merge") + colnames(merge.123)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(merge.123)))] = paste(colnames(merge.123)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(merge.123), perl=T))], ".z", sep="") + #merge.123$thresholdValue = pmax(merge.123[,onx], merge.123[,ony], merge.123[,onz]) + + patientMerge = rbind(patientMerge, merge.123) + patient.fuzzy = patient.fuzzy[!(match.filter.1 | match.filter.2 | match.filter.3),] + + hidden.clone.sequences = c(rows.1[-1,"Clone_Sequence"], rows.2[rows.2$Clone_Sequence != first.clone.sequence,"Clone_Sequence"], rows.3[rows.3$Clone_Sequence != first.clone.sequence,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + } else if (matches.in.2) { + #other.sample1 = other.sample.list[[first.sample]][1] + #other.sample2 = other.sample.list[[first.sample]][2] + + second.sample = sum.2[,"Sample"] + + current.merge.list = duo.merge.list[[paste(first.sample, second.sample)]] + + merge.12 = merge(sum.1, sum.2, by="merge") + + current.merge.list = rbind(current.merge.list, merge.12) + duo.merge.list[[paste(first.sample, second.sample)]] = current.merge.list + + patient.fuzzy = patient.fuzzy[!(match.filter.1 | match.filter.2),] + + hidden.clone.sequences = c(rows.1[-1,"Clone_Sequence"], rows.2[rows.2$Clone_Sequence != first.clone.sequence,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + } else if (matches.in.3) { + + #other.sample1 = other.sample.list[[first.sample]][1] + #other.sample2 = other.sample.list[[first.sample]][2] + + second.sample = sum.3[,"Sample"] + + current.merge.list = duo.merge.list[[paste(first.sample, second.sample)]] + + merge.13 = merge(sum.1, sum.3, by="merge") + + current.merge.list = rbind(current.merge.list, merge.13) + duo.merge.list[[paste(first.sample, second.sample)]] = current.merge.list + + patient.fuzzy = patient.fuzzy[!(match.filter.1 | match.filter.3),] + + hidden.clone.sequences = c(rows.1[-1,"Clone_Sequence"], rows.3[rows.3$Clone_Sequence != first.clone.sequence,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + } else if(nrow(rows.1) > 1){ + patient1 = patient1[!(patient1$Clone_Sequence %in% rows.1$Clone_Sequence),] + print(names(patient1)[names(patient1) %in% sum.1]) + print(names(patient1)[!(names(patient1) %in% sum.1)]) + print(names(patient1)) + print(names(sum.1)) + print(summary(sum.1)) + print(summary(patient1)) + print(dim(sum.1)) + print(dim(patient1)) + print(head(sum.1[,names(patient1)])) + patient1 = rbind(patient1, sum.1[,names(patient1)]) + patient.fuzzy = patient.fuzzy[-match.filter.1,] + } else { + patient.fuzzy = patient.fuzzy[-1,] + } + + tmp.rows = rbind(rows.1, rows.2, rows.3) + tmp.rows = tmp.rows[order(nchar(tmp.rows$Clone_Sequence)),] + + if (sum(match.filter.1) > 1 | sum(match.filter.2) > 1 | sum(match.filter.1) > 1) { + cat(paste("<tr><td>", label1, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$Sample, ":</td><td>", tmp.rows$Clone_Sequence, "</td><td>", tmp.rows$normalized_read_count, "</td></tr>", sep=""), file="multiple_matches.html", append=T) + } else { + } + + } + patient.merge.list[[paste(label1, "123")]] = patientMerge + + patientMerge12 = duo.merge.list[[paste(oneSample, twoSample)]] + patientMerge13 = duo.merge.list[[paste(oneSample, threeSample)]] + patientMerge23 = duo.merge.list[[paste(twoSample, threeSample)]] + + patient.merge.list[[paste(label1, "12")]] = patientMerge12 + patient.merge.list[[paste(label1, "13")]] = patientMerge13 + patient.merge.list[[paste(label1, "23")]] = patientMerge23 + + patient.merge.list.second[[label1]] = merge.list[["second"]] + } + cat(paste("<td>", nrow(patient1), " in ", label1, " and ", nrow(patient2), " in ", label2, nrow(patient3), " in ", label3, ", ", nrow(patientMerge), " in both (finding both took ", (proc.time() - start.time)[[3]], "s)</td></tr>", sep=""), file=logfile, append=T) + patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + patientMerge12$thresholdValue = pmax(patientMerge12[,onx], patientMerge12[,ony]) + patientMerge13$thresholdValue = pmax(patientMerge13[,onx], patientMerge13[,ony]) + patientMerge23$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony]) + + #patientMerge$thresholdValue = pmin(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + #patientMerge12$thresholdValue = pmin(patientMerge12[,onx], patientMerge12[,ony]) + #patientMerge13$thresholdValue = pmin(patientMerge13[,onx], patientMerge13[,ony]) + #patientMerge23$thresholdValue = pmin(patientMerge23[,onx], patientMerge23[,ony]) + + patient1 = patient1[!(patient1$Clone_Sequence %in% merge.list[["second"]]),] + patient2 = patient2[!(patient2$Clone_Sequence %in% merge.list[["second"]]),] + patient3 = patient3[!(patient3$Clone_Sequence %in% merge.list[["second"]]),] + + if(F){ + patientMerge = merge(patient1, patient2, by="merge") + patientMerge = merge(patientMerge, patient3, by="merge") + colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge)))] = paste(colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.y$)|(merge)", names(patientMerge), perl=T))], ".z", sep="") + patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + patientMerge12 = merge(patient1, patient2, by="merge") + patientMerge12$thresholdValue = pmax(patientMerge12[,onx], patientMerge12[,ony]) + patientMerge13 = merge(patient1, patient3, by="merge") + patientMerge13$thresholdValue = pmax(patientMerge13[,onx], patientMerge13[,ony]) + patientMerge23 = merge(patient2, patient3, by="merge") + patientMerge23$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony]) + } + + scatterplot_data_columns = c("Clone_Sequence", "Frequency", "normalized_read_count", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "merge") + scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns], patient3[,scatterplot_data_columns]) + scatterplot_data = scatterplot_data[!duplicated(scatterplot_data$merge),] + scatterplot_data$type = factor(x="In one", levels=c("In one", "In two", "In three", "In multiple")) + + res1 = vector() + res2 = vector() + res3 = vector() + res12 = vector() + res13 = vector() + res23 = vector() + resAll = vector() + read1Count = vector() + read2Count = vector() + read3Count = vector() + + if(appendTriplets){ + cat(paste(label1, label2, label3, sep="\t"), file="triplets.txt", append=T, sep="", fill=3) + } + for(iter in 1:length(product[,1])){ + threshhold = product[iter,threshholdIndex] + V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="") + J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="") + #all = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge[,onx] > threshhold & patientMerge[,ony] > threshhold & patientMerge[,onz] > threshhold) + all = (grepl(V_Segment, patientMerge$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge$J_Segment_Major_Gene.x) & patientMerge$thresholdValue > threshhold) + + one_two = (grepl(V_Segment, patientMerge12$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge12$J_Segment_Major_Gene.x) & patientMerge12$thresholdValue > threshhold & !(patientMerge12$merge %in% patientMerge[all,]$merge)) + one_three = (grepl(V_Segment, patientMerge13$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge13$J_Segment_Major_Gene.x) & patientMerge13$thresholdValue > threshhold & !(patientMerge13$merge %in% patientMerge[all,]$merge)) + two_three = (grepl(V_Segment, patientMerge23$V_Segment_Major_Gene.x) & grepl(J_Segment, patientMerge23$J_Segment_Major_Gene.x) & patientMerge23$thresholdValue > threshhold & !(patientMerge23$merge %in% patientMerge[all,]$merge)) + + one = (grepl(V_Segment, patient1$V_Segment_Major_Gene) & grepl(J_Segment, patient1$J_Segment_Major_Gene) & patient1[,on] > threshhold & !(patient1$merge %in% patientMerge[all,]$merge) & !(patient1$merge %in% patientMerge12[one_two,]$merge) & !(patient1$merge %in% patientMerge13[one_three,]$merge)) + two = (grepl(V_Segment, patient2$V_Segment_Major_Gene) & grepl(J_Segment, patient2$J_Segment_Major_Gene) & patient2[,on] > threshhold & !(patient2$merge %in% patientMerge[all,]$merge) & !(patient2$merge %in% patientMerge12[one_two,]$merge) & !(patient2$merge %in% patientMerge23[two_three,]$merge)) + three = (grepl(V_Segment, patient3$V_Segment_Major_Gene) & grepl(J_Segment, patient3$J_Segment_Major_Gene) & patient3[,on] > threshhold & !(patient3$merge %in% patientMerge[all,]$merge) & !(patient3$merge %in% patientMerge13[one_three,]$merge) & !(patient3$merge %in% patientMerge23[two_three,]$merge)) + + read1Count = append(read1Count, sum(patient1[one,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.x)) + read2Count = append(read2Count, sum(patient2[two,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.y)) + read3Count = append(read3Count, sum(patient3[three,]$normalized_read_count) + sum(patientMerge[all,]$normalized_read_count.z)) + res1 = append(res1, sum(one)) + res2 = append(res2, sum(two)) + res3 = append(res3, sum(three)) + resAll = append(resAll, sum(all)) + res12 = append(res12, sum(one_two)) + res13 = append(res13, sum(one_three)) + res23 = append(res23, sum(two_three)) + #threshhold = 0 + if(threshhold != 0){ + if(sum(one) > 0){ + dfOne = patient1[one,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")] + colnames(dfOne) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone") + filenameOne = paste(label1, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfOne, file=paste(filenameOne, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(two) > 0){ + dfTwo = patient2[two,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")] + colnames(dfTwo) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone") + filenameTwo = paste(label2, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfTwo, file=paste(filenameTwo, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(three) > 0){ + dfThree = patient3[three,c("V_Segment_Major_Gene", "J_Segment_Major_Gene", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone")] + colnames(dfThree) = c("Proximal segment", "Distal segment", "normalized_read_count", "Frequency", "Clone_Sequence", "Related_to_leukemia_clone") + filenameThree = paste(label3, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfThree, file=paste(filenameThree, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(one_two) > 0){ + dfOne_two = patientMerge12[one_two,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")] + colnames(dfOne_two) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample)) + filenameOne_two = paste(label1, "_", label2, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="") + write.table(dfOne_two, file=paste(filenameOne_two, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(one_three) > 0){ + dfOne_three = patientMerge13[one_three,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")] + colnames(dfOne_three) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample)) + filenameOne_three = paste(label1, "_", label3, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="") + write.table(dfOne_three, file=paste(filenameOne_three, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + if(sum(two_three) > 0){ + dfTwo_three = patientMerge23[two_three,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y")] + colnames(dfTwo_three) = c(paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample),"Clone_Sequence", paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample)) + filenameTwo_three = paste(label2, "_", label3, "_", product[iter, titleIndex], "_", threshhold, onShort, sep="") + write.table(dfTwo_three, file=paste(filenameTwo_three, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + } else { #scatterplot data + scatterplot_locus_data = scatterplot_data[grepl(V_Segment, scatterplot_data$V_Segment_Major_Gene) & grepl(J_Segment, scatterplot_data$J_Segment_Major_Gene),] + scatterplot_locus_data = scatterplot_locus_data[!(scatterplot_locus_data$merge %in% merge.list[["second"]]),] + in_two = (scatterplot_locus_data$merge %in% patientMerge12[one_two,]$merge) | (scatterplot_locus_data$merge %in% patientMerge13[one_three,]$merge) | (scatterplot_locus_data$merge %in% patientMerge23[two_three,]$merge) + if(sum(in_two) > 0){ + scatterplot_locus_data[in_two,]$type = "In two" + } + in_three = (scatterplot_locus_data$merge %in% patientMerge[all,]$merge) + if(sum(in_three)> 0){ + scatterplot_locus_data[in_three,]$type = "In three" + } + not_in_one = scatterplot_locus_data$type != "In one" + if(sum(not_in_one) > 0){ + #scatterplot_locus_data[not_in_one,]$type = "In multiple" + } + p = NULL + if(nrow(scatterplot_locus_data) != 0){ + if(on == "normalized_read_count"){ + scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count)))) + p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales, limits=c(1, 1e6)) + } else { + p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_log10(limits=c(0.0001,100), breaks=c(0.0001, 0.001, 0.01, 0.1, 1, 10, 100), labels=c("0.0001", "0.001", "0.01", "0.1", "1", "10", "100")) + expand_limits(y=c(0,100)) + #p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + } + p = p + geom_point(aes(colour=type), position="jitter") + p = p + xlab("In one or in multiple samples") + ylab(onShort) + ggtitle(paste(label1, label2, label3, onShort, product[iter, titleIndex])) + } else { + p = ggplot(NULL, aes(x=c("In one", "In multiple"),y=0)) + geom_blank(NULL) + xlab("In two or in three of the samples") + ylab(onShort) + ggtitle(paste(label1, label2, label3, onShort, product[iter, titleIndex])) + } + png(paste(label1, "_", label2, "_", label3, "_", onShort, "_", product[iter, titleIndex],"_scatter.png", sep="")) + print(p) + dev.off() + } + if(sum(all) > 0){ + dfAll = patientMerge[all,c("V_Segment_Major_Gene.x", "J_Segment_Major_Gene.x", "normalized_read_count.x", "Frequency.x", "Related_to_leukemia_clone.x", "Clone_Sequence.x", "V_Segment_Major_Gene.y", "J_Segment_Major_Gene.y", "normalized_read_count.y", "Frequency.y", "Related_to_leukemia_clone.y", "V_Segment_Major_Gene.z", "J_Segment_Major_Gene.z", "normalized_read_count.z", "Frequency.z", "Related_to_leukemia_clone.z")] + colnames(dfAll) = c(paste("Proximal segment", oneSample), paste("Distal segment", oneSample), paste("Normalized_Read_Count", oneSample), paste("Frequency", oneSample), paste("Related_to_leukemia_clone", oneSample),"Clone_Sequence", paste("Proximal segment", twoSample), paste("Distal segment", twoSample), paste("Normalized_Read_Count", twoSample), paste("Frequency", twoSample), paste("Related_to_leukemia_clone", twoSample), paste("Proximal segment", threeSample), paste("Distal segment", threeSample), paste("Normalized_Read_Count", threeSample), paste("Frequency", threeSample), paste("Related_to_leukemia_clone", threeSample)) + filenameAll = paste(label1, "_", label2, "_", label3, "_", product[iter, titleIndex], "_", threshhold, sep="") + write.table(dfAll, file=paste(filenameAll, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + } + } + #patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "All"=resAll, "tmp1"=res1, "read_count1" = round(read1Count), "tmp2"=res2, "read_count2"= round(read2Count), "tmp3"=res3, "read_count3"=round(read3Count)) + patientResult = data.frame("Locus"=product$Titles, "J_Segment"=product$J_Segments, "V_Segment"=product$V_Segments, "cut_off_value"=paste(">", product$interval, sep=""), "All"=resAll, "tmp1"=res1, "tmp2"=res2, "tmp3"=res3, "tmp12"=res12, "tmp13"=res13, "tmp23"=res23) + colnames(patientResult)[6] = oneSample + colnames(patientResult)[7] = twoSample + colnames(patientResult)[8] = threeSample + colnames(patientResult)[9] = paste(oneSample, twoSample, sep="_") + colnames(patientResult)[10] = paste(oneSample, twoSample, sep="_") + colnames(patientResult)[11] = paste(oneSample, twoSample, sep="_") + + colnamesBak = colnames(patientResult) + colnames(patientResult) = c("Ig/TCR gene rearrangement type", "Distal Gene segment", "Proximal gene segment", "cut_off_value", "Number of sequences All", paste("Number of sequences", oneSample), paste("Number of sequences", twoSample), paste("Number of sequences", threeSample), paste("Number of sequences", oneSample, twoSample), paste("Number of sequences", oneSample, threeSample), paste("Number of sequences", twoSample, threeSample)) + write.table(patientResult, file=paste(label1, "_", label2, "_", label3, "_", onShort, ".txt", sep=""), quote=F, sep="\t", dec=",", row.names=F, col.names=T) + colnames(patientResult) = colnamesBak + + patientResult$Locus = factor(patientResult$Locus, Titles) + patientResult$cut_off_value = factor(patientResult$cut_off_value, paste(">", interval, sep="")) + + plt = ggplot(patientResult[,c("Locus", "cut_off_value", "All")]) + plt = plt + geom_bar( aes( x=factor(cut_off_value), y=All), stat='identity', position="dodge", fill="#79c36a") + plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + plt = plt + geom_text(aes(ymax=max(All), x=cut_off_value,y=All,label=All), angle=90, hjust=0) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in All") + plt = plt + theme(plot.margin = unit(c(1,8.8,0.5,1.5), "lines")) + png(paste(label1, "_", label2, "_", label3, "_", onShort, "_total_all.png", sep=""), width=1920, height=1080) + print(plt) + dev.off() + + fontSize = 4 + + bak = patientResult + patientResult = melt(patientResult[,c('Locus','cut_off_value', oneSample, twoSample, threeSample)] ,id.vars=1:2) + patientResult$relativeValue = patientResult$value * 10 + patientResult[patientResult$relativeValue == 0,]$relativeValue = 1 + plt = ggplot(patientResult) + plt = plt + geom_bar( aes( x=factor(cut_off_value), y=relativeValue, fill=variable), stat='identity', position="dodge") + plt = plt + facet_grid(.~Locus) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + plt = plt + scale_y_continuous(trans="log", breaks=10^c(0:10), labels=c(0, 10^c(0:9))) + plt = plt + geom_text(data=patientResult[patientResult$variable == oneSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=-0.7, size=fontSize) + plt = plt + geom_text(data=patientResult[patientResult$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.4, size=fontSize) + plt = plt + geom_text(data=patientResult[patientResult$variable == threeSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=1.5, size=fontSize) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle("Number of clones in only one sample") + png(paste(label1, "_", label2, "_", label3, "_", onShort, "_indiv_all.png", sep=""), width=1920, height=1080) + print(plt) + dev.off() +} + +if(nrow(triplets) != 0){ + + cat("<tr><td>Starting triplet analysis</td></tr>", file=logfile, append=T) + + triplets$uniqueID = "ID" + + triplets[grepl("16278_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left" + triplets[grepl("26402_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left" + triplets[grepl("26759_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left" + + triplets[grepl("16278_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right" + triplets[grepl("26402_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right" + triplets[grepl("26759_Right", triplets$Sample),]$uniqueID = "16278_26402_26759_Right" + + triplets[grepl("14696", triplets$Patient),]$uniqueID = "14696" + + cat("<tr><td>Normalizing to lowest cell count within locus</td></tr>", file=logfile, append=T) + + triplets$locus_V = substring(triplets$V_Segment_Major_Gene, 0, 4) + triplets$locus_J = substring(triplets$J_Segment_Major_Gene, 0, 4) + min_cell_count = data.frame(data.table(triplets)[, list(min_cell_count=min(.SD$Cell_Count)), by=c("uniqueID", "locus_V", "locus_J")]) + + triplets$min_cell_paste = paste(triplets$uniqueID, triplets$locus_V, triplets$locus_J) + min_cell_count$min_cell_paste = paste(min_cell_count$uniqueID, min_cell_count$locus_V, min_cell_count$locus_J) + + min_cell_count = min_cell_count[,c("min_cell_paste", "min_cell_count")] + + triplets = merge(triplets, min_cell_count, by="min_cell_paste") + + triplets$normalized_read_count = round(triplets$Clone_Molecule_Count_From_Spikes / triplets$Cell_Count * triplets$min_cell_count / 2, digits=2) #??????????????????????????????????? wel of geen / 2 + + triplets = triplets[triplets$normalized_read_count >= min_cells,] + + column_drops = c("min_cell_count", "min_cell_paste") + + triplets = triplets[,!(colnames(triplets) %in% column_drops)] + + cat("<tr><td>Starting Cell Count analysis</td></tr>", file=logfile, append=T) + + interval = intervalReads + intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) + product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) + + one = triplets[triplets$Sample == "14696_reg_BM",] + two = triplets[triplets$Sample == "24536_reg_BM",] + three = triplets[triplets$Sample == "24062_reg_BM",] + tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="normalized_read_count", T) + + one = triplets[triplets$Sample == "16278_Left",] + two = triplets[triplets$Sample == "26402_Left",] + three = triplets[triplets$Sample == "26759_Left",] + tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="normalized_read_count", T) + + one = triplets[triplets$Sample == "16278_Right",] + two = triplets[triplets$Sample == "26402_Right",] + three = triplets[triplets$Sample == "26759_Right",] + tripletAnalysis(one, "16278_Right_Trio", two, "26402_Right_Trio", three, "26759_Right_Trio", product=product, interval=interval, on="normalized_read_count", T) + + cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T) + + interval = intervalFreq + intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) + product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) + + one = triplets[triplets$Sample == "14696_reg_BM",] + two = triplets[triplets$Sample == "24536_reg_BM",] + three = triplets[triplets$Sample == "24062_reg_BM",] + tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="Frequency", F) + + one = triplets[triplets$Sample == "16278_Left",] + two = triplets[triplets$Sample == "26402_Left",] + three = triplets[triplets$Sample == "26759_Left",] + tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="Frequency", F) + + one = triplets[triplets$Sample == "16278_Right",] + two = triplets[triplets$Sample == "26402_Right",] + three = triplets[triplets$Sample == "26759_Right",] + tripletAnalysis(one, "16278_Right_Trio", two, "26402_Right_Trio", three, "26759_Right_Trio", product=product, interval=interval, on="Frequency", F) +} else { + cat("", file="triplets.txt") +} +cat("</table></html>", file=logfile, append=T)
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/script.js Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,86 @@ +function tsvToTable(data){ + var lines = data.split("\n"); + var tbl = document.createElement('table'); + tbl.className = "result_table"; + tbl.border="1" + var thead = document.createElement('thead'); + var tr = document.createElement('tr'); + tr.className = "evenrowcolor"; + var cells = lines[0].split("\t"); + var cdr3column = []; + for(var a = 0;a < cells.length;++a){ + if(cells[a] == "CDR3 Sequence" || cells[a] == "CDR3_Sense_Sequence" || cells[a] == "Clone Sequence"){ + cdr3column.push(a); + } + var td = document.createElement('td'); + td.appendChild(document.createTextNode(cells[a])); + tr.appendChild(td); + } + thead.appendChild(tr); + tbl.appendChild(thead); + var tbdy = document.createElement('tbody'); + + for(var a = 1;a < lines.length;++a){ + tr = document.createElement('tr'); + var cells = lines[a].split("\t"); + if(cells.length == 1){ + continue; + } + for(var b = 0;b < cells.length;++b){ + td = document.createElement('td'); + td.appendChild(document.createTextNode(cells[b])); + if(cdr3column.indexOf(b) != -1){ + td.className = td.className + " cdr3sequence" + } + tr.appendChild(td) + } + + if(a % 2 == 0){ + tr.className = "evenrowcolor"; + } else { + tr.className = "oddrowcolor"; + } + + tbdy.appendChild(tr); + } + tbl.appendChild(tbdy); + return tbl; +} + +function loadfile(file, patient, type){ + patient = patient.replace(".", "\\."); + $('#hidden_div').load(file, function(){ + $('#result_div_' + patient + '_' + type).html(tsvToTable($('#hidden_div').html())); + $('#result_div_' + patient + '_' + type + ' tr').hover(function() { + $(this).addClass('hover'); + }, function() { + $(this).removeClass('hover'); + }); + $('#result_div_' + patient + '_' + type + ' table').addClass('result_table'); + //$('#result_div_' + patient + ' tr:odd').addClass("oddrowcolor"); + //$('#result_div_' + patient + ' tr:even').addClass("evenrowcolor"); + $('#result_div_' + patient + '_' + type + ' table').before( "<a href='" + file + "'>Download " + file.replace(".txt", "") + "</a>" ); + }); +} + +var currentTD = new Array(); + +$( document ).ready(function() { + $('.summary_table tr').hover(function() { + $(this).addClass('hover'); + }, function() { + $(this).removeClass('hover'); + }); + + $('.summary_table tr:odd').addClass("oddrowcolor"); + $('.summary_table tr:even').addClass("evenrowcolor"); + + $('.summary_table td[data-patient]').click(function() { + var tmp = $(this); + if(currentTD[tmp.attr("data-patient")] != null){ + currentTD[tmp.attr("data-patient")].removeClass("clicked_summary"); + } + currentTD[tmp.attr("data-patient")] = tmp; + currentTD[tmp.attr("data-patient")].addClass("clicked_summary"); + }); +});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/style.css Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,150 @@ +/* $Id: example.css,v 1.5 2006/03/27 02:44:36 pat Exp $ */ + +/*-------------------------------------------------- + REQUIRED to hide the non-active tab content. + But do not hide them in the print stylesheet! + --------------------------------------------------*/ +.tabberlive .tabbertabhide { + display:none; +} + +/*-------------------------------------------------- + .tabber = before the tabber interface is set up + .tabberlive = after the tabber interface is set up + --------------------------------------------------*/ +.tabber { +} +.tabberlive { + margin-top:1em; +} + +/*-------------------------------------------------- + ul.tabbernav = the tab navigation list + li.tabberactive = the active tab + --------------------------------------------------*/ +ul.tabbernav +{ + margin:0; + padding: 3px 0; + border-bottom: 1px solid #778; + font: bold 12px Verdana, sans-serif; +} + +ul.tabbernav li +{ + list-style: none; + margin: 0; + display: inline; +} + +ul.tabbernav li a +{ + padding: 3px 0.5em; + margin-left: 3px; + border: 1px solid #778; + border-bottom: none; + background: #DDE; + text-decoration: none; +} + +ul.tabbernav li a:link { color: #448; } +ul.tabbernav li a:visited { color: #667; } + +ul.tabbernav li a:hover +{ + color: #000; + background: #AAE; + border-color: #227; +} + +ul.tabbernav li.tabberactive a +{ + background-color: #fff; + border-bottom: 1px solid #fff; +} + +ul.tabbernav li.tabberactive a:hover +{ + color: #000; + background: white; + border-bottom: 1px solid white; +} + +/*-------------------------------------------------- + .tabbertab = the tab content + Add style only after the tabber interface is set up (.tabberlive) + --------------------------------------------------*/ +.tabberlive .tabbertab { + padding:5px; + border:1px solid #aaa; + border-top:0; + + /* If you don't want the tab size changing whenever a tab is changed + you can set a fixed height */ + + /* height:200px; */ + + /* If you set a fix height set overflow to auto and you will get a + scrollbar when necessary */ + + /* overflow:auto; */ +} + +/* If desired, hide the heading since a heading is provided by the tab */ +.tabberlive .tabbertab h2 { + display:none; +} +.tabberlive .tabbertab h3 { + display:none; +} + +/* Example of using an ID to set different styles for the tabs on the page */ +.tabberlive#tab1 { +} +.tabberlive#tab2 { +} +.tabberlive#tab2 .tabbertab { + height:200px; + overflow:auto; +} + +.result_table tr:hover { + background-color:#999999; +} + +table.result_table { + font-family: verdana,arial,sans-serif; + font-size:11px; + color:#333333; + border-width: 1px; + border-color: #666666; + border-collapse: collapse; +} +table.result_table th { + border-width: 1px; + padding: 8px; + border-style: solid; + border-color: #666666; +} +table.result_table td { + border-width: 1px; + padding: 8px; + border-style: solid; + border-color: #666666; +} + +.clicked_summary { + background-color:#A0A0A0; +} + +.oddrowcolor{ + background-color:#F4F4F4; +} + +.evenrowcolor{ + background-color:#E5E5E5; +} + +.cdr3sequence { + text-align: right; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tabber.js Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,40 @@ +/* Copyright (c) 2006 Patrick Fitzgerald */ + +function tabberObj(argsObj) +{var arg;this.div=null;this.classMain="tabber";this.classMainLive="tabberlive";this.classTab="tabbertab";this.classTabDefault="tabbertabdefault";this.classNav="tabbernav";this.classTabHide="tabbertabhide";this.classNavActive="tabberactive";this.titleElements=['h2','h3','h4','h5','h6'];this.titleElementsStripHTML=true;this.removeTitle=true;this.addLinkId=false;this.linkIdFormat='<tabberid>nav<tabnumberone>';for(arg in argsObj){this[arg]=argsObj[arg];} +this.REclassMain=new RegExp('\\b'+this.classMain+'\\b','gi');this.REclassMainLive=new RegExp('\\b'+this.classMainLive+'\\b','gi');this.REclassTab=new RegExp('\\b'+this.classTab+'\\b','gi');this.REclassTabDefault=new RegExp('\\b'+this.classTabDefault+'\\b','gi');this.REclassTabHide=new RegExp('\\b'+this.classTabHide+'\\b','gi');this.tabs=new Array();if(this.div){this.init(this.div);this.div=null;}} +tabberObj.prototype.init=function(e) +{var +childNodes,i,i2,t,defaultTab=0,DOM_ul,DOM_li,DOM_a,aId,headingElement;if(!document.getElementsByTagName){return false;} +if(e.id){this.id=e.id;} +this.tabs.length=0;childNodes=e.childNodes;for(i=0;i<childNodes.length;i++){if(childNodes[i].className&&childNodes[i].className.match(this.REclassTab)){t=new Object();t.div=childNodes[i];this.tabs[this.tabs.length]=t;if(childNodes[i].className.match(this.REclassTabDefault)){defaultTab=this.tabs.length-1;}}} +DOM_ul=document.createElement("ul");DOM_ul.className=this.classNav;for(i=0;i<this.tabs.length;i++){t=this.tabs[i];t.headingText=t.div.title;if(this.removeTitle){t.div.title='';} +if(!t.headingText){for(i2=0;i2<this.titleElements.length;i2++){headingElement=t.div.getElementsByTagName(this.titleElements[i2])[0];if(headingElement){t.headingText=headingElement.innerHTML;if(this.titleElementsStripHTML){t.headingText.replace(/<br>/gi," ");t.headingText=t.headingText.replace(/<[^>]+>/g,"");} +break;}}} +if(!t.headingText){t.headingText=i+1;} +DOM_li=document.createElement("li");t.li=DOM_li;DOM_a=document.createElement("a");DOM_a.appendChild(document.createTextNode(t.headingText));DOM_a.href="javascript:void(null);";DOM_a.title=t.headingText;DOM_a.onclick=this.navClick;DOM_a.tabber=this;DOM_a.tabberIndex=i;if(this.addLinkId&&this.linkIdFormat){aId=this.linkIdFormat;aId=aId.replace(/<tabberid>/gi,this.id);aId=aId.replace(/<tabnumberzero>/gi,i);aId=aId.replace(/<tabnumberone>/gi,i+1);aId=aId.replace(/<tabtitle>/gi,t.headingText.replace(/[^a-zA-Z0-9\-]/gi,''));DOM_a.id=aId;} +DOM_li.appendChild(DOM_a);DOM_ul.appendChild(DOM_li);} +e.insertBefore(DOM_ul,e.firstChild);e.className=e.className.replace(this.REclassMain,this.classMainLive);this.tabShow(defaultTab);if(typeof this.onLoad=='function'){this.onLoad({tabber:this});} +return this;};tabberObj.prototype.navClick=function(event) +{var +rVal,a,self,tabberIndex,onClickArgs;a=this;if(!a.tabber){return false;} +self=a.tabber;tabberIndex=a.tabberIndex;a.blur();if(typeof self.onClick=='function'){onClickArgs={'tabber':self,'index':tabberIndex,'event':event};if(!event){onClickArgs.event=window.event;} +rVal=self.onClick(onClickArgs);if(rVal===false){return false;}} +self.tabShow(tabberIndex);return false;};tabberObj.prototype.tabHideAll=function() +{var i;for(i=0;i<this.tabs.length;i++){this.tabHide(i);}};tabberObj.prototype.tabHide=function(tabberIndex) +{var div;if(!this.tabs[tabberIndex]){return false;} +div=this.tabs[tabberIndex].div;if(!div.className.match(this.REclassTabHide)){div.className+=' '+this.classTabHide;} +this.navClearActive(tabberIndex);return this;};tabberObj.prototype.tabShow=function(tabberIndex) +{var div;if(!this.tabs[tabberIndex]){return false;} +this.tabHideAll();div=this.tabs[tabberIndex].div;div.className=div.className.replace(this.REclassTabHide,'');this.navSetActive(tabberIndex);if(typeof this.onTabDisplay=='function'){this.onTabDisplay({'tabber':this,'index':tabberIndex});} +return this;};tabberObj.prototype.navSetActive=function(tabberIndex) +{this.tabs[tabberIndex].li.className=this.classNavActive;return this;};tabberObj.prototype.navClearActive=function(tabberIndex) +{this.tabs[tabberIndex].li.className='';return this;};function tabberAutomatic(tabberArgs) +{var +tempObj,divs,i;if(!tabberArgs){tabberArgs={};} +tempObj=new tabberObj(tabberArgs);divs=document.getElementsByTagName("div");for(i=0;i<divs.length;i++){if(divs[i].className&&divs[i].className.match(tempObj.REclassMain)){tabberArgs.div=divs[i];divs[i].tabber=new tabberObj(tabberArgs);}} +return this;} +function tabberAutomaticOnLoad(tabberArgs) +{var oldOnLoad;if(!tabberArgs){tabberArgs={};} +oldOnLoad=window.onload;if(typeof window.onload!='function'){window.onload=function(){tabberAutomatic(tabberArgs);};}else{window.onload=function(){oldOnLoad();tabberAutomatic(tabberArgs);};}} +if(typeof tabberOptions=='undefined'){tabberAutomaticOnLoad();}else{if(!tabberOptions['manualStartup']){tabberAutomaticOnLoad(tabberOptions);}} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/wrapper.sh Wed Aug 31 05:31:47 2016 -0400 @@ -0,0 +1,348 @@ +#!/bin/bash + +inputFile=$1 +outputFile=$2 +outputDir=$3 +min_freq=$4 +min_cells=$5 +merge_on="$6" + +dir="$(cd "$(dirname "$0")" && pwd)" +mkdir $outputDir + + +Rscript --verbose $dir/RScript.r $inputFile $outputDir $outputFile $min_freq $min_cells "${merge_on}" 2>&1 +cp $dir/jquery-1.11.0.min.js $outputDir +cp $dir/script.js $outputDir +cp $dir/style.css $outputDir +cp $dir/tabber.js $outputDir +mv "$outputFile" "$outputDir/log.html" + +echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)</center></html>" > $outputFile + +cd $outputDir + +header="<html><head><script type='text/javascript' src='jquery-1.11.0.min.js'></script><script type='text/javascript' src='tabber.js'></script><script type='text/javascript' src='script.js'></script><link rel='stylesheet' type='text/css' href='style.css'></head><div id='hidden_div' style='display: none;'></div>" +singles=() +pairs_BM_PB=() +pairs_Left_Right=() +pairs_R_Dx=() +while read patient sample1 sample2 type +do + echo "$patient" + html="${patient}.html" + echo "$header" > "$html" + if [[ "$type" == *pair* ]] ; then + if [[ "$sample1" == *_BM* ]] || [[ "$sample1" == *_PB* ]] ; then + pairs_BM_PB+=( "$patient" ) + elif [[ "$sample1" == *_Left* ]] || [[ "$sample1" == *_Right* ]] ; then + pairs_Left_Right+=( "$patient" ) + else + pairs_R_Dx+=( "$patient" ) + fi + else + singles+=( "$patient" ) + fi + oldLocus="" + sample1="$(echo ${sample1} | tr -d '\r' | tr -d '\n')" + sample2="$(echo ${sample2} | tr -d '\r' | tr -d '\n')" + tail -n+2 ${patient}_freq.txt | sed "s/>//" > tmp.txt + echo "<div class='tabber'>" >> "$html" + echo "<div class='tabbertab' title='Data frequency'>" >> "$html" + echo "<table><tr><td style='vertical-align:top;'>" >> "$html" + echo "<table border = 1 class='result_table summary_table' id='summary_table_${patient}_freq'>" >> "$html" + echo "<thead><th>Ig/TCR gene rearrangement type</th><th>Proximal gene segment</th><th>Distal gene segment</th><th>Cut off value</th><th>Number of sequences ${patient}_Both</th><th>Number of sequences_$sample1</th><th>Read Count $sample1</th><th>Number of sequences_$sample2</th><th>Read Count $sample2</th><th>Sum number of sequences $patient</th><th>Percentage of sequences ${patient}_both</th></thead>" >> "$html" + echo "<tbody>" >> "$html" + scatterplot_tab="<div class='tabbertab' title='Scatter Plots Frequency'><table border='0'><tr>" + while read locus j_segment v_segment cut_off_value both one read_count1 two read_count2 sum percent locusreadsum1 locusreadsum2 + do + if [ "$locus" != "$oldLocus" ] ; then + echo "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr>" >> "$html" + echo "<tr><td><b>$locus</b></td>" >> "$html" + else + echo "<td></td>" >> "$html" + fi + echo "<td>$v_segment</td>" >> "$html" + echo "<td>$j_segment</td>" >> "$html" + echo "<td>>$cut_off_value</td>" >> "$html" + if [ "$both" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$both</td>" >> "$html" + else + echo "<td>$both</td>" >> "$html" + fi + if [ "$one" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$one</td>" >> "$html" + else + echo "<td>$one</td>" >> "$html" + fi + echo "<td>$read_count1</td>" >> "$html" + if [ "$two" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$two</td>" >> "$html" + else + echo "<td>$two</td>" >> "$html" + fi + echo "<td>$read_count2</td>" >> "$html" + echo "<td>$sum</td>" >> "$html" + echo "<td>${percent}%</td>" >> "$html" + echo "</tr>" >> "$html" + oldLocus="$locus" + if [ "${cut_off_value}" == "0" ] ; then + scatterplot_tab="${scatterplot_tab}<td><img src='${patient}_${sample1}_${sample2}_freq_${locus}_scatter.png' /></td>" + fi + done < tmp.txt + echo "</tbody></table>" >> "$html" + echo "</td><td style='vertical-align:top;'><div id='result_div_${patient}_freq'></div></td></tr></table>" >> "$html" + echo "</div>" >> "$html" + echo "<div class='tabbertab' title='Graphs frequency'>" >> "$html" + echo "<a href='${patient}_freq.png'><img src='${patient}_freq.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_freq_both.png'><img src='${patient}_freq_both.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_percent_freq.png'><img src='${patient}_percent_freq.png' width='1280' height='720' /></a></div>" >> "$html" + echo "${scatterplot_tab}</tr></table></div>" >> "$html" + + tail -n+2 ${patient}_reads.txt | sed "s/>//" > tmp.txt + echo "<div class='tabbertab' title='Data reads'>" >> "$html" + echo "<table><tr><td style='vertical-align:top;'>" >> "$html" + echo "<table border = 1 class='result_table summary_table' id='summary_table_${patient}_reads'>" >> "$html" + echo "<thead><th>Ig/TCR gene rearrangement type</th><th>Proximal gene segment</th><th>Distal gene segment</th><th>Cut off value</th><th>Number of sequences ${patient}_Both</th><th>Number of sequences_$sample1</th><th>Read Count $sample1</th><th>Number of sequences_$sample2</th><th>Read Count $sample2</th><th>Sum number of sequences $patient</th><th>Percentage of sequences ${patient}_both</th></thead>" >> "$html" + echo "<tbody>" >> "$html" + scatterplot_tab="<div class='tabbertab' title='Scatter Plots Reads'><table border='0'><tr>" + while read locus j_segment v_segment cut_off_value both one read_count1 two read_count2 sum percent locusreadsum1 locusreadsum2 + do + if [ "$locus" != "$oldLocus" ] ; then + echo "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr>" >> "$html" + echo "<tr><td><b>$locus</b></td>" >> "$html" + else + echo "<td></td>" >> "$html" + fi + echo "<td>$v_segment</td>" >> "$html" + echo "<td>$j_segment</td>" >> "$html" + echo "<td>>$cut_off_value</td>" >> "$html" + if [ "$both" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$both</td>" >> "$html" + else + echo "<td>$both</td>" >> "$html" + fi + if [ "$one" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$one</td>" >> "$html" + else + echo "<td>$one</td>" >> "$html" + fi + echo "<td>$read_count1</td>" >> "$html" + if [ "$two" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$two</td>" >> "$html" + else + echo "<td>$two</td>" >> "$html" + fi + echo "<td>$read_count2</td>" >> "$html" + echo "<td>$sum</td>" >> "$html" + echo "<td>${percent}%</td>" >> "$html" + echo "</tr>" >> "$html" + oldLocus="$locus" + if [ "${cut_off_value}" == "0" ] ; then + scatterplot_tab="${scatterplot_tab}<td><img src='${patient}_${sample1}_${sample2}_reads_${locus}_scatter.png' /></td>" + fi + done < tmp.txt + echo "</tbody></table>" >> "$html" + echo "</td><td style='vertical-align:top;'><div id='result_div_${patient}_reads'></div></td></tr></table>" >> "$html" + echo "</div>" >> "$html" + echo "<div class='tabbertab' title='Graphs reads'>" >> "$html" + echo "<a href='${patient}_reads.png'><img src='${patient}_reads.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_reads_both.png'><img src='${patient}_reads_both.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_percent_reads.png'><img src='${patient}_percent_reads.png' width='1280' height='720' /></a></div>" >> "$html" + echo "${scatterplot_tab}</tr></table></div>" >> "$html" + echo "</div>" >> "$html" + echo "</div>" >> "$html" + echo "</html>" >> "$html" +done < patients.txt + +html="index.html" +echo "<html>" > $html +echo "<table>" >> "$html" +echo "<tr><td><b>Singles (<a href='singles_freq_scatterplot.png'>Frequency scatterplot</a>, <a href='singles_reads_scatterplot.png'>Reads scatterplot</a>):</b></td></tr>" >> "$html" +for patient in "${singles[@]}" +do + echo "<tr><td><a href='${patient}.html'>$patient</a></td></tr>" >> "$html" +done +echo "<tr><td><b>Pairs (Left & Right):</b></td></tr>" >> "$html" +for patient in "${pairs_Left_Right[@]}" +do + echo "<tr><td><a href='${patient}.html'>$patient</a></td></tr>" >> "$html" +done +echo "<tr><td><b>Pairs (BM & PB):</b></td></tr>" >> "$html" +for patient in "${pairs_BM_PB[@]}" +do + echo "<tr><td><a href='${patient}.html'>$patient</a></td></tr>" >> "$html" +done +echo "<tr><td><b>Pairs (Dx & R):</b></td></tr>" >> "$html" +for patient in "${pairs_R_Dx[@]}" +do + echo "<tr><td><a href='${patient}.html'>$patient</a></td></tr>" >> "$html" +done +echo "<tr><td><b>Triplets:</b></td></tr>" >> "$html" + +while read sample1 sample2 sample3 +do + sample1="$(echo ${sample1} | tr -d '\r' | tr -d '\n')" + sample2="$(echo ${sample2} | tr -d '\r' | tr -d '\n')" + sample3="$(echo ${sample3} | tr -d '\r' | tr -d '\n')" + patient="${sample1}_${sample2}_${sample3}" + echo "$patient" + html="${patient}.html" + echo "<tr><td><a href='${patient}.html'>$patient</a></td></tr>" >> "index.html" + echo "$header" > $html + oldLocus="" + tail -n+2 ${patient}_freq.txt | sed "s/>//" > tmp.txt + echo "<div class='tabber'>" >> "$html" + echo "<div class='tabbertab' title='Data frequency'>" >> "$html" + echo "<table><tr><td style='vertical-align:top;'>" >> "$html" + echo "<table border = 1 class='result_table summary_table' id='summary_table_${patient}_freq'>" >> "$html" + echo "<thead><th>Ig/TCR gene rearrangement type</th><th>Proximal gene segment</th><th>Distal gene segment</th><th>Cut off value</th><th>Number of sequences ${patient}_All</th><th>Number of sequences_$sample1</th><th>Number of sequences_$sample2</th><th>Number of sequences_$sample3</th><th>Number of sequences_${sample1}_${sample2}</th><th>Number of sequences_${sample1}_${sample3}</th><th>Number of sequences_${sample2}_${sample3}</th></thead>" >> "$html" + echo "<tbody>" >> "$html" + scatterplot_tab="<div class='tabbertab' title='Scatter Plots Frequency'><table border='0'><tr>" + while read locus j_segment v_segment cut_off_value all one two three one_two one_three two_three + do + if [ "$locus" != "$oldLocus" ] ; then + echo "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr>" >> "$html" + echo "<tr><td><b>$locus</b></td>" >> "$html" + else + echo "<td></td>" >> "$html" + fi + echo "<td>$v_segment</td>" >> "$html" + echo "<td>$j_segment</td>" >> "$html" + echo "<td>>$cut_off_value</td>" >> "$html" + if [ "$all" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${sample3}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$all</td>" >> "$html" + else + echo "<td>$all</td>" >> "$html" + fi + if [ "$one" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$one</td>" >> "$html" + else + echo "<td>$one</td>" >> "$html" + fi + if [ "$two" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$two</td>" >> "$html" + else + echo "<td>$two</td>" >> "$html" + fi + if [ "$three" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample3}_${locus}_${cut_off_value}.txt\", \"$patient\", \"freq\")'>$three</td>" >> "$html" + else + echo "<td>$three</td>" >> "$html" + fi + + if [ "${one_two}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${locus}_${cut_off_value}freq.txt\", \"$patient\", \"freq\")'>${one_two}</td>" >> "$html" + else + echo "<td>${one_two}</td>" >> "$html" + fi + if [ "${one_three}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample3}_${locus}_${cut_off_value}freq.txt\", \"$patient\", \"freq\")'>${one_three}</td>" >> "$html" + else + echo "<td>${one_three}</td>" >> "$html" + fi + if [ "${two_three}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${sample3}_${locus}_${cut_off_value}freq.txt\", \"$patient\", \"freq\")'>${two_three}</td>" >> "$html" + else + echo "<td>${two_three}</td>" >> "$html" + fi + + echo "</tr>" >> "$html" + oldLocus="$locus" + if [ "${cut_off_value}" == "0" ] ; then + scatterplot_tab="${scatterplot_tab}<td><img src='${sample1}_${sample2}_${sample3}_freq_${locus}_scatter.png' /></td>" + fi + done < tmp.txt + echo "</tbody></table>" >> "$html" + echo "</td><td style='vertical-align:top;'><div id='result_div_${patient}_freq'></div></td></tr></table>" >> "$html" + echo "</div>" >> "$html" + echo "<div class='tabbertab' title='Graphs frequency'>" >> "$html" + echo "<a href='${patient}_freq_total_all.png'><img src='${patient}_freq_total_all.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_freq_indiv_all.png'><img src='${patient}_freq_indiv_all.png' width='1280' height='720' /></a><br /></div>" >> "$html" + echo "${scatterplot_tab}</tr></table></div>" >> "$html" + + tail -n+2 ${patient}_reads.txt | sed "s/>//" > tmp.txt + echo "<div class='tabbertab' title='Data reads'>" >> "$html" + echo "<table><tr><td style='vertical-align:top;'>" >> "$html" + echo "<table border = 1 class='result_table summary_table' id='summary_table_${patient}_reads'>" >> "$html" + echo "<thead><th>Ig/TCR gene rearrangement type</th><th>Proximal gene segment</th><th>Distal gene segment</th><th>Cut off value</th><th>Number of sequences ${patient}_All</th><th>Number of sequences_$sample1</th><th>Number of sequences_$sample2</th><th>Number of sequences_$sample3</th><th>Number of sequences_${sample1}_${sample2}</th><th>Number of sequences_${sample1}_${sample3}</th><th>Number of sequences_${sample2}_${sample3}</th></thead>" >> "$html" + echo "<tbody>" >> "$html" + scatterplot_tab="<div class='tabbertab' title='Scatter Plots Reads'><table border='0'><tr>" + while read locus j_segment v_segment cut_off_value all one two three one_two one_three two_three + do + if [ "$locus" != "$oldLocus" ] ; then + echo "<tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr>" >> "$html" + echo "<tr><td><b>$locus</b></td>" >> "$html" + else + echo "<td></td>" >> "$html" + fi + echo "<td>$v_segment</td>" >> "$html" + echo "<td>$j_segment</td>" >> "$html" + echo "<td>>$cut_off_value</td>" >> "$html" + if [ "$all" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${sample3}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$all</td>" >> "$html" + else + echo "<td>$all</td>" >> "$html" + fi + if [ "$one" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$one</td>" >> "$html" + else + echo "<td>$one</td>" >> "$html" + fi + if [ "$two" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$two</td>" >> "$html" + else + echo "<td>$two</td>" >> "$html" + fi + if [ "$three" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample3}_${locus}_${cut_off_value}.txt\", \"$patient\", \"reads\")'>$three</td>" >> "$html" + else + echo "<td>$three</td>" >> "$html" + fi + + if [ "${one_two}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample2}_${locus}_${cut_off_value}reads.txt\", \"$patient\", \"reads\")'>${one_two}</td>" >> "$html" + else + echo "<td>${one_two}</td>" >> "$html" + fi + if [ "${one_three}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample1}_${sample3}_${locus}_${cut_off_value}reads.txt\", \"$patient\", \"reads\")'>${one_three}</td>" >> "$html" + else + echo "<td>${one_three}</td>" >> "$html" + fi + if [ "${two_three}" != "0" ] && [ "$cut_off_value" != "0" ] ; then + echo "<td data-patient='${patient}' style='cursor:pointer' onclick='javascript:loadfile(\"${sample2}_${sample3}_${locus}_${cut_off_value}reads.txt\", \"$patient\", \"reads\")'>${two_three}</td>" >> "$html" + else + echo "<td>${two_three}</td>" >> "$html" + fi + + echo "</tr>" >> "$html" + oldLocus="$locus" + if [ "${cut_off_value}" == "0" ] ; then + scatterplot_tab="${scatterplot_tab}<td><img src='${sample1}_${sample2}_${sample3}_reads_${locus}_scatter.png' /></td>" + fi + done < tmp.txt + echo "</tbody></table>" >> "$html" + echo "</td><td style='vertical-align:top;'><div id='result_div_${patient}_reads'></div></td></tr></table>" >> "$html" + echo "</div>" >> "$html" + echo "<div class='tabbertab' title='Graphs reads'>" >> "$html" + echo "<a href='${patient}_reads_total_all.png'><img src='${patient}_reads_total_all.png' width='1280' height='720' /></a><br />" >> "$html" + echo "<a href='${patient}_reads_indiv_all.png'><img src='${patient}_reads_indiv_all.png' width='1280' height='720' /></a><br /></div>" >> "$html" + echo "${scatterplot_tab}</tr></table></div>" >> "$html" + echo "</div>" >> "$html" + echo "</div>" >> "$html" + echo "</html>" >> "$html" +done < triplets.txt +rm tmp.txt + + +html="index.html" + +echo "</table>" >> "$html" +echo "<a href='log.html'>log</a><br />" >> "$html" +echo "<a href='single_matches.html'>single_matches</a><br />" >> "$html" +echo "<a href='multiple_matches.html'>multiple_matches</a><br />" >> "$html" +echo "</html>" >> "$html" + +cp "index.html" "$outputFile" +