Mercurial > repos > davidvanzessen > clonal_sequences_in_paired_samples
changeset 60:28c3695259c1 draft
Uploaded
| author | davidvanzessen |
|---|---|
| date | Thu, 29 Oct 2015 11:21:33 -0400 |
| parents | 36e307208f1b |
| children | 77a090cd0e02 |
| files | RScript.r RScript.txt |
| diffstat | 2 files changed, 1026 insertions(+), 32 deletions(-) [+] |
line wrap: on
line diff
--- a/RScript.r Thu Oct 15 05:34:17 2015 -0400 +++ b/RScript.r Thu Oct 29 11:21:33 2015 -0400 @@ -442,17 +442,14 @@ 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) +lapply(patients[4], 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") - -cat("</table></html>", file=logfile, append=T) - +lapply(patients[4], 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$normalized_read_count)))) @@ -483,6 +480,10 @@ print(p) dev.off() } + +patient.merge.list = list() #cache the 'both' table, 2x speedup for more memory... +patient.merge.list.second = list() + tripletAnalysis <- function(patient1, label1, patient2, label2, patient3, label3, product, interval, on, appendTriplets= FALSE){ onShort = "reads" if(on == "Frequency"){ @@ -502,7 +503,7 @@ oneSample = paste(patient1[1,sampleIndex], sep="") twoSample = paste(patient2[1,sampleIndex], sep="") threeSample = paste(patient3[1,sampleIndex], sep="") - + if(mergeOn == "Clone_Sequence"){ patient1$merge = paste(patient1$Clone_Sequence) patient2$merge = paste(patient2$Clone_Sequence) @@ -513,18 +514,249 @@ patient2$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) + + 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$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + #patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony], patientMerge[,onz]) + patientMerge = patientMerge[NULL,] + + duo.merge.list = list() + patientMerge12 = merge(patient1, patient2, by="merge") - patientMerge12$thresholdValue = pmax(patientMerge12[,onx], patientMerge12[,ony]) + #patientMerge12$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$thresholdValue = pmax(patientMerge13[,onx], patientMerge13[,ony]) + #patientMerge13$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$Frequency), + locus_V = rows.1[1,"locus_V"], + locus_J = rows.1[1,"locus_J"], + normalized_read_count = sum(rows.1$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$Frequency), + locus_V = rows.2[1,"locus_V"], + locus_J = rows.2[1,"locus_J"], + normalized_read_count = sum(rows.2$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$Frequency), + locus_V = rows.3[1,"locus_V"], + locus_J = rows.3[1,"locus_J"], + normalized_read_count = sum(rows.3$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$Clone_Sequence != first.clone.sequence,"Clone_Sequence"]) + merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) + + } else { + patient.fuzzy = patient.fuzzy[-1,] + } + + tmp.rows = rbind(rows.1, rows.2, rows.3) + tmp.rows = tmp.rows[order(nchar(tmp.rows$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$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony]) - + + if(F){ + patientMerge = merge(patient1, patient2, by="merge") + patientMerge = merge(patientMerge, patient3, by="merge") + colnames(patientMerge)[which(!grepl("(\\.x$)|(\\.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$thresholdValue = pmax(patientMerge23[,onx], patientMerge23[,ony]) + } scatterplot_data_columns = c("Clone_Sequence", "Frequency", "normalized_read_count", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "merge") scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns], patient3[,scatterplot_data_columns]) @@ -610,6 +842,7 @@ } } else { #scatterplot data scatterplot_locus_data = scatterplot_data[grepl(V_Segment, scatterplot_data$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,]$type = "In two" @@ -693,6 +926,9 @@ } if(nrow(triplets) != 0){ + + cat("<tr><td>Starting triplet analysis</td></tr>", file=logfile, append=T) + triplets$uniqueID = "ID" triplets[grepl("16278_Left", triplets$Sample),]$uniqueID = "16278_26402_26759_Left" @@ -704,7 +940,9 @@ 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$Cell_Count)), by=c("uniqueID", "locus_V", "locus_J")]) @@ -720,23 +958,12 @@ triplets = triplets[triplets$normalized_read_count >= min_cells,] - column_drops = c("locus_V", "locus_J", "min_cell_count", "min_cell_paste") + column_drops = c("min_cell_count", "min_cell_paste") triplets = triplets[,!(colnames(triplets) %in% column_drops)] - - #remove duplicate V+J+CDR3, add together numerical values - triplets = data.frame(data.table(triplets)[, list(Receptor=unique(.SD$Receptor), - Cell_Count=unique(.SD$Cell_Count), - Clone_Molecule_Count_From_Spikes=sum(.SD$Clone_Molecule_Count_From_Spikes), - Total_Read_Count=sum(.SD$Total_Read_Count), - dsPerM=ifelse("dsPerM" %in% names(dat), sum(.SD$dsPerM), 0), - Related_to_leukemia_clone=all(.SD$Related_to_leukemia_clone), - Frequency=sum(.SD$Frequency), - normalized_read_count=sum(.SD$normalized_read_count), - Log10_Frequency=sum(.SD$Log10_Frequency), - Clone_Sequence=.SD$Clone_Sequence[1]), by=c("Patient", "Sample", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "CDR3_Sense_Sequence")]) - - + + 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))) @@ -744,19 +971,20 @@ one = triplets[triplets$Sample == "14696_reg_BM",] two = triplets[triplets$Sample == "24536_reg_BM",] three = triplets[triplets$Sample == "24062_reg_BM",] - tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="normalized_read_count", T) + #tripletAnalysis(one, "14696_1_Trio", two, "14696_2_Trio", three, "14696_3_Trio", product=product, interval=interval, on="normalized_read_count", T) one = triplets[triplets$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) + #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$Sample == "26759_Right",] tripletAnalysis(one, "16278_Right_Trio", two, "26402_Right_Trio", three, "26759_Right_Trio", product=product, interval=interval, on="normalized_read_count", T) - + cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T) + interval = intervalFreq intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) @@ -764,12 +992,12 @@ one = triplets[triplets$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) + #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$Sample == "26759_Left",] - tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="Frequency", F) + #tripletAnalysis(one, "16278_Left_Trio", two, "26402_Left_Trio", three, "26759_Left_Trio", product=product, interval=interval, on="Frequency", F) one = triplets[triplets$Sample == "16278_Right",] two = triplets[triplets$Sample == "26402_Right",] @@ -778,3 +1006,4 @@ } else { cat("", file="triplets.txt") } +cat("</table></html>", file=logfile, append=T) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/RScript.txt Thu Oct 29 11:21:33 2015 -0400 @@ -0,0 +1,765 @@ +args <- commandArgs(trailingOnly = TRUE) + +inFile = args[1] +outDir = args[2] +logfile = args[3] +min_freq = as.numeric(args[4]) +min_cells = as.numeric(args[5]) +mergeOn = args[6] + +cat("<html><table><tr><td>Starting analysis</td></tr>", file=logfile, append=F) + +library(ggplot2) +library(reshape2) +library(data.table) +library(grid) +library(parallel) +#require(xtable) +cat("<tr><td>Reading input</td></tr>", file=logfile, append=T) +dat = read.table(inFile, header=T, sep="\t", dec=".", fill=T, stringsAsFactors=F) +dat = dat[,c("Patient", "Receptor", "Sample", "Cell_Count", "Clone_Molecule_Count_From_Spikes", "Log10_Frequency", "Total_Read_Count", "J_Segment_Major_Gene", "V_Segment_Major_Gene", "CDR3_Sense_Sequence", "Related_to_leukemia_clone", "Clone_Sequence")] +dat$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$Patient, drop=T) +intervalReads = rev(c(0,10,25,50,100,250,500,750,1000,10000)) +intervalFreq = rev(c(0,0.01,0.05,0.1,0.5,1,5)) +V_Segments = c(".*", "IGHV", "IGHD", "IGKV", "IGKV", "IgKINTR", "TRGV", "TRDV", "TRDD" , "TRBV") +J_Segments = c(".*", ".*", ".*", "IGKJ", "KDE", ".*", ".*", ".*", ".*", ".*") +Titles = c("Total", "IGH-Vh-Jh", "IGH-Dh-Jh", "Vk-Jk", "Vk-Kde" , "Intron-Kde", "TCRG", "TCRD-Vd-Dd", "TCRD-Dd-Dd", "TCRB-Vb-Jb") +Titles = factor(Titles, levels=Titles) +TitlesOrder = data.frame("Title"=Titles, "TitlesOrder"=1:length(Titles)) + +single_patients = data.frame("Patient" = character(0),"Sample" = character(0), "on" = character(0), "Clone_Sequence" = character(0), "Frequency" = numeric(0), "normalized_read_count" = numeric(0), "V_Segment_Major_Gene" = character(0), "J_Segment_Major_Gene" = character(0), "Rearrangement" = character(0)) + +patient.merge.list = list() #cache the 'both' table, 2x speedup for more memory... +patient.merge.list.second = list() +cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="multiple_matches.html", append=T) +cat(paste("<table border='0' style='font-family:courier;'>", sep=""), file="single_matches.html", append=T) +patientCountOnColumn <- function(x, product, interval, on, appendtxt=F){ + if (!is.data.frame(x) & is.list(x)){ + x = x[[1]] + } + #x$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 = scatterplot_data[!duplicated(scatterplot_data$merge),] + scatterplot_data$type = factor(x=oneSample, levels=c(oneSample, twoSample, "In Both")) + scatterplot_data$on = onShort + + #patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge") #merge alles 'fuzzy' + patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge")[NULL,] #blegh + + cs.exact.matches = patient1[patient1$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]] + 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$Clone_Sequence)),] + + merge.list = list() + + merge.list[["second"]] = vector() + + while(nrow(patient.fuzzy) > 1){ + first.merge = patient.fuzzy[1,"merge"] + first.clone.sequence = patient.fuzzy[1,"Clone_Sequence"] + first.sample = patient.fuzzy[1,"Sample"] + merge.filter = first.merge == patient.fuzzy$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$Clone_Sequence)),] + + if (nrow(first.rows) > 1 | nrow(second.rows) > 1) { + cat(paste("<tr><td>", patient, " row ", 1:nrow(tmp.rows), "</td><td>", tmp.rows$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 { + patient.fuzzy = patient.fuzzy[-1,] + } + } + patient.merge.list[[patient]] <<- patientMerge + patient.merge.list.second[[patient]] <<- merge.list[["second"]] + 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$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony]) + res1 = vector() + res2 = vector() + resBoth = vector() + read1Count = vector() + read2Count = vector() + locussum1 = vector() + locussum2 = vector() + + #for(iter in 1){ + for(iter in 1:length(product[,1])){ + threshhold = product[iter,threshholdIndex] + V_Segment = paste(".*", as.character(product[iter,V_SegmentIndex]), ".*", sep="") + J_Segment = paste(".*", as.character(product[iter,J_SegmentIndex]), ".*", sep="") + #both = (grepl(V_Segment, patientMerge$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),] + #scatterplot_locus_data = scatterplot_locus_data[!(scatterplot_locus_data$merge %in% merge.list[[twoSample]]),] + scatterplot_locus_data = scatterplot_locus_data[!(scatterplot_locus_data$merge %in% merge.list[["second"]]),] + if(nrow(scatterplot_locus_data) > 0){ + scatterplot_locus_data$Rearrangement = product[iter, titleIndex] + } + in_one = (scatterplot_locus_data$merge %in% patient1$merge) + in_two = (scatterplot_locus_data$merge %in% patient2$merge) + if(any(in_two)){ + scatterplot_locus_data[in_two,]$type = twoSample + } + in_both = (scatterplot_locus_data$merge %in% patientMerge$merge) + #merge.list.filter = (scatterplot_locus_data$merge %in% merge.list[[oneSample]]) + #exact.matches.filter = (scatterplot_locus_data$merge %in% cs.exact.matches) + if(any(in_both)){ + scatterplot_locus_data[in_both,]$type = "In Both" + } + if(type == "single"){ + single_patients <<- rbind(single_patients, scatterplot_locus_data) + } + p = NULL + if(nrow(scatterplot_locus_data) != 0){ + if(on == "normalized_read_count"){ + scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count)))) + p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=10^6) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + } else { + p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + } + p = p + geom_point(aes(colour=type), position="jitter") + 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$variable == twoSample,], aes(ymax=max(value), x=cut_off_value,y=relativeValue,label=value), angle=90, position=position_dodge(width=0.9), hjust=0, vjust=0.8) + plt = plt + xlab("Reads per locus") + ylab("Count") + ggtitle(paste("Number of clones in only ", oneSample, " and only ", twoSample, sep="")) + png(paste(patient, "_", onShort, "_both.png", sep=""), width=1920, height=1080) + print(plt) + dev.off() +} + +cat("<tr><td>Starting Frequency analysis</td></tr>", file=logfile, append=T) + +interval = intervalFreq +intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) +product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) +lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="Frequency", appendtxt=T) + +cat("<tr><td>Starting Cell Count analysis</td></tr>", file=logfile, append=T) + +interval = intervalReads +intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) +product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) +lapply(patients, FUN=patientCountOnColumn, product = product, interval=interval, on="normalized_read_count") + +cat("</table></html>", file=logfile, append=T) + + +if(nrow(single_patients) > 0){ + scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$normalized_read_count)))) + p = ggplot(single_patients, aes(Rearrangement, normalized_read_count)) + scale_y_log10(breaks=scales,labels=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$Patient)) + 100, height=1080) + print(p) + dev.off() + + p = ggplot(single_patients, aes(Rearrangement, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + p = p + geom_point(aes(colour=type), position="jitter") + p = p + xlab("In one or both samples") + ylab("Frequency") + p = p + facet_grid(.~Patient) + ggtitle("Scatterplot of the frequency of the patients with a single sample") + png("singles_freq_scatterplot.png", width=640 * length(unique(single_patients$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() +} +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") + 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),] + 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) + expand_limits(y=c(0,1000000)) + } else { + 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){ + 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),]$uniqueID = "14696" + + triplets$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$normalized_read_count >= min_cells,] + + column_drops = c("locus_V", "locus_J", "min_cell_count", "min_cell_paste") + + triplets = triplets[,!(colnames(triplets) %in% column_drops)] + + #remove duplicate V+J+CDR3, add together numerical values + triplets = data.frame(data.table(triplets)[, list(Receptor=unique(.SD$Receptor), + Cell_Count=unique(.SD$Cell_Count), + Clone_Molecule_Count_From_Spikes=sum(.SD$Clone_Molecule_Count_From_Spikes), + Total_Read_Count=sum(.SD$Total_Read_Count), + dsPerM=ifelse("dsPerM" %in% names(dat), sum(.SD$dsPerM), 0), + Related_to_leukemia_clone=all(.SD$Related_to_leukemia_clone), + Frequency=sum(.SD$Frequency), + normalized_read_count=sum(.SD$normalized_read_count), + Log10_Frequency=sum(.SD$Log10_Frequency), + Clone_Sequence=.SD$Clone_Sequence[1]), by=c("Patient", "Sample", "V_Segment_Major_Gene", "J_Segment_Major_Gene", "CDR3_Sense_Sequence")]) + + + interval = intervalReads + intervalOrder = data.frame("interval"=paste(">", interval, sep=""), "intervalOrder"=1:length(interval)) + product = data.frame("Titles"=rep(Titles, each=length(interval)), "interval"=rep(interval, times=10), "V_Segments"=rep(V_Segments, each=length(interval)), "J_Segments"=rep(J_Segments, each=length(interval))) + + one = triplets[triplets$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) + + + 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") +}