Mercurial > repos > davidvanzessen > clonal_sequences_in_paired_samples
changeset 68:ef13f0a3f4d6 draft
Uploaded
| author | davidvanzessen |
|---|---|
| date | Tue, 05 Jan 2016 10:49:40 -0500 |
| parents | 40c72b9ffc79 |
| children | c532b3f8dc97 |
| files | RScript.r RScript.txt script.js style.css |
| diffstat | 4 files changed, 104 insertions(+), 798 deletions(-) [+] |
line wrap: on
line diff
--- a/RScript.r Fri Nov 20 11:41:30 2015 -0500 +++ b/RScript.r Tue Jan 05 10:49:40 2016 -0500 @@ -1,4 +1,5 @@ args <- commandArgs(trailingOnly = TRUE) +options(scipen=999) inFile = args[1] outDir = args[2] @@ -67,6 +68,7 @@ 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){ @@ -125,10 +127,15 @@ } 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$on = onShort + #scatterplot_data = rbind(patient1[,scatterplot_data_columns], patient2[,scatterplot_data_columns]) + scatterplot_data = patient1[NULL,scatterplot_data_columns] + #scatterplot_data = scatterplot_data[!duplicated(scatterplot_data$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$on = character(0) #patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge") #merge alles 'fuzzy' patientMerge = merge(patient1, patient2, by.x="merge", by.y="merge")[NULL,] #blegh @@ -141,6 +148,7 @@ if(patient %in% names(patient.merge.list)){ patientMerge = patient.merge.list[[patient]] merge.list[["second"]] = patient.merge.list.second[[patient]] + scatterplot_data = scatter_locus_data_list[[patient]] cat(paste("<td>", nrow(patient1), " in ", oneSample, " and ", nrow(patient2), " in ", twoSample, ", ", nrow(patientMerge), " in both (fetched from cache)</td></tr>", sep=""), file=logfile, append=T) print(names(patient.merge.list)) @@ -175,7 +183,9 @@ merge.list = list() merge.list[["second"]] = vector() - + + link.count = 1 + while(nrow(patient.fuzzy) > 1){ first.merge = patient.fuzzy[1,"merge"] first.clone.sequence = patient.fuzzy[1,"Clone_Sequence"] @@ -264,7 +274,24 @@ tmp.rows = rbind(first.rows, second.rows) tmp.rows = tmp.rows[order(nchar(tmp.rows$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$normalized_read_count, "</td></tr>", sep=""), file="multiple_matches.html", append=T) } else { @@ -289,14 +316,32 @@ merge.list[["second"]] = append(merge.list[["second"]], hidden.clone.sequences) patient.fuzzy = patient.fuzzy[-first.match.filter,] + + #add to the scatterplot data + scatterplot.row = first.sum[,scatterplot_data_columns] + scatterplot.row$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"]] + 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) } @@ -305,6 +350,7 @@ patientMerge$thresholdValue = pmax(patientMerge[,onx], patientMerge[,ony]) + #patientMerge$thresholdValue = pmin(patientMerge[,onx], patientMerge[,ony]) res1 = vector() res2 = vector() resBoth = vector() @@ -346,33 +392,42 @@ } 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"]]),] + #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) - } + + + + #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" & (nrow(scatterplot_locus_data) > 0 | !any(scatterplot_locus_data$Patient %in% single_patients$Patient))){ + # single_patients <<- rbind(single_patients, scatterplot_locus_data) + #} + + p = NULL + print(paste("nrow scatterplot_locus_data", nrow(scatterplot_locus_data))) 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$type), drop=FALSE) + #p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count)) + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=10^6) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + p = ggplot(scatterplot_locus_data, aes(type, normalized_read_count, group=link)) + geom_line() + scale_y_log10(breaks=scales,labels=scales) + expand_limits(y=c(0,10^6)) + scale_x_discrete(breaks=levels(scatterplot_data$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$type), drop=FALSE) + #p = ggplot(scatterplot_locus_data, aes(type, Frequency)) + scale_y_continuous(limits = c(0, 100)) + expand_limits(y=c(0,100)) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) + p = ggplot(scatterplot_locus_data, aes(type, Frequency, group=link)) + geom_line() + scale_y_log10(limits=c(0.0001,100), breaks=c(0.0001, 0.001, 0.01, 0.1, 1, 10, 100), labels=c("0.0001", "0.001", "0.01", "0.1", "1", "10", "100")) + scale_x_discrete(breaks=levels(scatterplot_data$type), labels=levels(scatterplot_data$type), drop=FALSE) } - p = p + geom_point(aes(colour=type), position="jitter") + #p = p + geom_point(aes(colour=type), position="jitter") + p = p + geom_point(aes(colour=type), position="dodge") p = p + xlab("In one or both samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex])) } else { p = ggplot(NULL, aes(x=c("In one", "In Both"),y=0)) + geom_blank(NULL) + xlab("In one or both of the samples") + ylab(onShort) + ggtitle(paste(patient1[1,patientIndex], patient1[1,sampleIndex], patient2[1,sampleIndex], onShort, product[iter, titleIndex])) @@ -453,7 +508,7 @@ if(nrow(single_patients) > 0){ scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$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 = 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") @@ -461,7 +516,8 @@ 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_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") @@ -762,6 +818,11 @@ 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$Clone_Sequence %in% merge.list[["second"]]),] @@ -882,7 +943,8 @@ scales = 10^(0:6) #(0:ceiling(log10(max(scatterplot_locus_data$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 = 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])) @@ -1027,4 +1089,4 @@ } else { cat("", file="triplets.txt") } -cat("</table></html>", file=logfile, append=T) \ No newline at end of file +cat("</table></html>", file=logfile, append=T)
--- a/RScript.txt Fri Nov 20 11:41:30 2015 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,765 +0,0 @@ -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") -}
--- a/script.js Fri Nov 20 11:41:30 2015 -0500 +++ b/script.js Tue Jan 05 10:49:40 2016 -0500 @@ -7,10 +7,10 @@ var tr = document.createElement('tr'); tr.className = "evenrowcolor"; var cells = lines[0].split("\t"); - var cdr3column = 0; + var cdr3column = []; for(var a = 0;a < cells.length;++a){ - if(cells[a] == "CDR3 Sequence" || cells[a] == "CDR3_Sense_Sequence"){ - cdr3column = 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])); @@ -29,13 +29,18 @@ 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);
--- a/style.css Fri Nov 20 11:41:30 2015 -0500 +++ b/style.css Tue Jan 05 10:49:40 2016 -0500 @@ -104,8 +104,8 @@ .tabberlive#tab2 { } .tabberlive#tab2 .tabbertab { - height:200px; - overflow:auto; + height:200px; + overflow:auto; } .result_table tr:hover { @@ -144,3 +144,7 @@ .evenrowcolor{ background-color:#E5E5E5; } + +.cdr3sequence { + text-align: right; +}