# HG changeset patch
# User davidvanzessen
# Date 1452008980 18000
# Node ID ef13f0a3f4d6c238c4625a608a173eddd9322738
# Parent  40c72b9ffc791fe60ecfd0e8d8f332a4eee8a64a
Uploaded

diff -r 40c72b9ffc79 -r ef13f0a3f4d6 RScript.r
--- 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)
diff -r 40c72b9ffc79 -r ef13f0a3f4d6 RScript.txt
--- 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")
-}
diff -r 40c72b9ffc79 -r ef13f0a3f4d6 script.js
--- 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);
diff -r 40c72b9ffc79 -r ef13f0a3f4d6 style.css
--- 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;
+}