# HG changeset patch
# User davidvanzessen
# Date 1486994926 18000
# Node ID 28fbbdfd7a875e2e029a3757b0a5fabc8d07a194
# Parent  94765af0db1fdf0f438913cceae4fe994966bbf1
Uploaded

diff -r 94765af0db1f -r 28fbbdfd7a87 experimental_design/experimental_design.py
--- a/experimental_design/experimental_design.py	Thu Feb 09 07:20:09 2017 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,50 +0,0 @@
-import sys
-import pandas as pd
-
-def main():
-	patients = {}
-	files = []
-	sample_id = sys.argv[1]
-	imgt_files = 0
-	blast_files = 0
-	#organize files
-	for arg in sys.argv[2:-2]:
-		if arg.find("/") is -1:
-			patients[sample_id] = files
-			files = []
-			sample_id = arg
-		else:
-			df = pd.read_csv(arg, sep="\t", dtype=object, error_bad_lines=False)
-			if "Functionality" in list(df.columns.values):
-				df["VDJ Frame"][df["Functionality"] != "productive"] = "In-frame with stop codon"
-				imgt_files += 1
-			else:
-				blast_files += 1
-			files.append(df)
-	patients[sample_id] = files
-	columns = [u'ID', u'VDJ Frame', u'Top V Gene', u'Top D Gene', u'Top J Gene', u'CDR1 Seq', u'CDR1 Length', u'CDR2 Seq', u'CDR2 Length', 
-			   u'CDR3 Seq', u'CDR3 Length', u'CDR3 Seq DNA', u'CDR3 Length DNA', u'Strand', u'CDR3 Found How', u'Functionality', 'V-REGION identity %', 
-			   'V-REGION identity nt', 'D-REGION reading frame', 'AA JUNCTION', 'Functionality comment', 'Sequence', 'FR1-IMGT', 'FR2-IMGT', 
-			   'FR3-IMGT', 'CDR3-IMGT', 'JUNCTION', 'J-REGION', 'FR4-IMGT', 'P3V-nt nb', 'N1-REGION-nt nb', 'P5D-nt nb', 'P3D-nt nb', 'N2-REGION-nt nb', 
-			   'P5J-nt nb', '3V-REGION trimmed-nt nb', '5D-REGION trimmed-nt nb', '3D-REGION trimmed-nt nb', '5J-REGION trimmed-nt nb', u'Sample', u'Replicate']
-	if "N-REGION-nt nb" in files[0].columns:
-		columns.insert(30, "N-REGION-nt nb")
-	if blast_files is not 0:
-		print "Has a parsed blastn file, using limited columns."
-		columns = [u'ID', u'VDJ Frame', u'Top V Gene', u'Top D Gene', u'Top J Gene', u'CDR1 Seq', u'CDR1 Length', u'CDR2 Seq', u'CDR2 Length', u'CDR3 Seq', u'CDR3 Length', u'CDR3 Seq DNA', u'CDR3 Length DNA', u'Strand', u'CDR3 Found How', u'Sample', u'Replicate']
-
-	result = None
-	for patient_id, samples in patients.iteritems():
-		count = 1
-		for sample in samples:
-			sample['Sample'] = patient_id
-			sample['Replicate'] = str(count)
-			count += 1
-			if result is None:
-				result = sample[columns]
-			else:
-				result = result.append(sample[columns])
-	result.to_csv(sys.argv[-1], sep="\t", index=False, index_label="index")
-
-if __name__ == "__main__":
-	main()
diff -r 94765af0db1f -r 28fbbdfd7a87 imgt_loader/imgt_loader.py
--- a/imgt_loader/imgt_loader.py	Thu Feb 09 07:20:09 2017 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,147 +0,0 @@
-import pandas as pd
-try:
-	pd.options.mode.chained_assignment = None  # default='warn'
-except:
-	pass
-import re
-import argparse
-import os
-
-def stop_err( msg, ret=1 ):
-    sys.stderr.write( msg )
-    sys.exit( ret )
-
-#docs.python.org/dev/library/argparse.html
-parser = argparse.ArgumentParser()
-parser.add_argument("--summ", help="The 1_Summary file from the imgt output")
-parser.add_argument("--aa", help="The 5_AA-Sequence file from the imgt output")
-parser.add_argument("--junction", help="The 6_Junction file from the imgt output")
-parser.add_argument("--output", help="Output file")
-
-args = parser.parse_args()
-
-old_summary_columns = [u'Sequence ID', u'JUNCTION frame', u'V-GENE and allele', u'D-GENE and allele', u'J-GENE and allele', u'CDR1-IMGT length', u'CDR2-IMGT length', u'CDR3-IMGT length', u'Orientation']
-old_sequence_columns = [u'CDR1-IMGT', u'CDR2-IMGT', u'CDR3-IMGT']
-old_junction_columns = [u'JUNCTION']
-
-added_summary_columns = [u'Functionality', u'V-REGION identity %', u'V-REGION identity nt', u'D-REGION reading frame', u'AA JUNCTION', u'Functionality comment', u'Sequence']
-added_sequence_columns = [u'FR1-IMGT', u'FR2-IMGT', u'FR3-IMGT', u'CDR3-IMGT', u'JUNCTION', u'J-REGION', u'FR4-IMGT']
-added_junction_columns = [u"P3'V-nt nb", u'N-REGION-nt nb', u'N1-REGION-nt nb', u"P5'D-nt nb", u"P3'D-nt nb", u'N2-REGION-nt nb', u"P5'J-nt nb", u"3'V-REGION trimmed-nt nb", 
-						  u"5'D-REGION trimmed-nt nb", u"3'D-REGION trimmed-nt nb", u"5'J-REGION trimmed-nt nb", u"N-REGION", u"N1-REGION", u"N2-REGION"]
-
-outFile = args.output
-
-#fSummary = pd.read_csv(triplets[0][0], sep="\t", low_memory=False)
-fSummary = pd.read_csv(args.summ, sep="\t", dtype=object)
-#fSequence = pd.read_csv(triplets[0][1], sep="\t", low_memory=False)
-fSequence = pd.read_csv(args.aa, sep="\t", dtype=object)
-#fJunction = pd.read_csv(triplets[0][2], sep="\t", low_memory=False)
-fJunction = pd.read_csv(args.junction, sep="\t", dtype=object)
-tmp = fSummary[["Sequence ID", "JUNCTION frame", "V-GENE and allele", "D-GENE and allele", "J-GENE and allele"]]
-
-tmp["CDR1 Seq"] = fSequence["CDR1-IMGT"]
-tmp["CDR1 Length"] = fSummary["CDR1-IMGT length"]
-
-tmp["CDR2 Seq"] = fSequence["CDR2-IMGT"]
-tmp["CDR2 Length"] = fSummary["CDR2-IMGT length"]
-
-tmp["CDR3 Seq"] = fSequence["CDR3-IMGT"]
-tmp["CDR3 Length"] = fSummary["CDR3-IMGT length"]
-
-tmp["CDR3 Seq DNA"] = fJunction["JUNCTION"]
-tmp["CDR3 Length DNA"] = '1'
-tmp["Strand"] = fSummary["Orientation"]
-tmp["CDR3 Found How"] = 'a'
-
-for col in added_summary_columns:
-    tmp[col] = fSummary[col]
-
-for col in added_sequence_columns:
-    tmp[col] = fSequence[col]
-
-for col in added_junction_columns:
-    tmp[col] = fJunction[col]
-
-outFrame = tmp
-
-outFrame.columns = [u'ID', u'VDJ Frame', u'Top V Gene', u'Top D Gene', u'Top J Gene', u'CDR1 Seq', u'CDR1 Length', u'CDR2 Seq', u'CDR2 Length', u'CDR3 Seq', u'CDR3 Length', 
-					u'CDR3 Seq DNA', u'CDR3 Length DNA', u'Strand', u'CDR3 Found How', u'Functionality', 'V-REGION identity %', 'V-REGION identity nt', 'D-REGION reading frame', 
-					'AA JUNCTION', 'Functionality comment', 'Sequence', 'FR1-IMGT', 'FR2-IMGT', 'FR3-IMGT', 'CDR3-IMGT', 'JUNCTION', 'J-REGION', 'FR4-IMGT', 'P3V-nt nb', 
-					'N-REGION-nt nb', 'N1-REGION-nt nb', 'P5D-nt nb', 'P3D-nt nb', 'N2-REGION-nt nb', 'P5J-nt nb', '3V-REGION trimmed-nt nb', '5D-REGION trimmed-nt nb', '3D-REGION trimmed-nt nb', 
-					'5J-REGION trimmed-nt nb', "N-REGION", "N1-REGION", "N2-REGION"]
-
-"""
-IGHV[0-9]-[0-9ab]+-?[0-9]?D?
-TRBV[0-9]{1,2}-?[0-9]?-?[123]?
-IGKV[0-3]D?-[0-9]{1,2}
-IGLV[0-9]-[0-9]{1,2}
-TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?
-TRGV[234589]
-TRDV[1-3]
-
-IGHD[0-9]-[0-9ab]+
-TRBD[12]
-TRDD[1-3]
-
-IGHJ[1-6]
-TRBJ[12]-[1-7]
-IGKJ[1-5]
-IGLJ[12367]
-TRAJ[0-9]{1,2}
-TRGJP?[12]
-TRDJ[1-4]
-"""
-
-vPattern = [r"(IGHV[0-9]-[0-9ab]+-?[0-9]?D?)",
-						r"(TRBV[0-9]{1,2}-?[0-9]?-?[123]?)",
-						r"(IGKV[0-3]D?-[0-9]{1,2})",
-						r"(IGLV[0-9]-[0-9]{1,2})",
-						r"(TRAV[0-9]{1,2}(-[1-46])?(/DV[45678])?)",
-						r"(TRGV[234589])",
-						r"(TRDV[1-3])",
-						r"(IGHV[0-9]S[0-9]+)"]
-
-dPattern = [r"(IGHD[0-9]-[0-9ab]+)",
-						r"(TRBD[12])",
-						r"(TRDD[1-3])"]
-						
-jPattern = [r"(IGHJ[1-6])",
-						r"(TRBJ[12]-[1-7])",
-						r"(IGKJ[1-5])",
-						r"(IGLJ[12367])",
-						r"(TRAJ[0-9]{1,2})",
-						r"(TRGJP?[12])",
-						r"(TRDJ[1-4])"]
-
-vPattern = re.compile(r"|".join(vPattern))
-
-dPattern = re.compile(r"|".join(dPattern))
-
-jPattern = re.compile(r"|".join(jPattern))
-
-
-def filterGenes(s, pattern):
-    if type(s) is not str:
-        return "NA"
-    res = pattern.search(s)
-    if res:
-        return res.group(0)
-    return "NA"
-
-
-
-outFrame["Top V Gene"] = outFrame["Top V Gene"].apply(lambda x: filterGenes(x, vPattern))
-outFrame["Top D Gene"] = outFrame["Top D Gene"].apply(lambda x: filterGenes(x, dPattern))
-outFrame["Top J Gene"] = outFrame["Top J Gene"].apply(lambda x: filterGenes(x, jPattern))
-
-
-tmp = outFrame["VDJ Frame"]
-tmp = tmp.replace("in-frame", "In-frame")
-tmp = tmp.replace("null", "Out-of-frame")
-tmp = tmp.replace("out-of-frame", "Out-of-frame")
-outFrame["VDJ Frame"] = tmp
-outFrame["CDR3 Length DNA"] = outFrame["CDR3 Seq DNA"].map(str).map(len)
-safeLength = lambda x: len(x) if type(x) == str else 0
-#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
-#outFrame = outFrame[(outFrame["CDR3 Seq DNA"].map(safeLength) > 0) & (outFrame["Top V Gene"] != "NA") & (outFrame["Top D Gene"] != "NA") & (outFrame["Top J Gene"] != "NA")] #filter out weird rows?
-outFrame.to_csv(outFile, sep="\t", index=False, index_label="index")
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/RScript.r
--- a/report_clonality/RScript.r	Thu Feb 09 07:20:09 2017 -0500
+++ b/report_clonality/RScript.r	Mon Feb 13 09:08:46 2017 -0500
@@ -614,86 +614,86 @@
       colnames(coincidence.table) = c("Coincidence Type",  "Raw Coincidence Freq")
       write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
     }
-  } else if(clonality_method == "old") {
-    clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
-    
-    #write files for every coincidence group of >1
-    samples = unique(clonalFreq$Sample)
-    for(sample in samples){
-		clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,]
-		if(max(clonalFreqSample$Type) > 1){
-			for(i in 2:max(clonalFreqSample$Type)){
-				clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,]
-				clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,]
-				clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),]
-				write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
-			}
-		}
-	}
-	
-    clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
-    clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
-    clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
-    clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
-    
-    ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
-    tcct = textConnection(ct)
-    CT  = read.table(tcct, sep="\t", header=TRUE)
-    close(tcct)
-    clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
-    clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
-    
-    ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
-    ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
-    clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
-    ReplicateReads$Reads = as.numeric(ReplicateReads$Reads)
-    ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads)
-    
-    ReplicatePrint <- function(dat){
-      write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
-    }
-    
-    ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
-    lapply(ReplicateSplit, FUN=ReplicatePrint)
-    
-    ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
-    clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
-    
-    ReplicateSumPrint <- function(dat){
-      write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
-    }
-    
-    ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
-    lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
-    
-    clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
-    clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
-    clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
-    clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
-    clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
-    
-    ClonalityScorePrint <- function(dat){
-      write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
-    }
-    
-    clonalityScore = clonalFreqCount[c("Sample", "Result")]
-    clonalityScore = unique(clonalityScore)
-    
-    clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
-    lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
-    
-    clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
-    
-    
-    
-    ClonalityOverviewPrint <- function(dat){
-	  dat = dat[order(dat[,2]),]
-      write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
-    }
-    
-    clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
-    lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
+  }
+  clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
+
+  #write files for every coincidence group of >1
+  samples = unique(clonalFreq$Sample)
+  for(sample in samples){
+      clonalFreqSample = clonalFreq[clonalFreq$Sample == sample,]
+      if(max(clonalFreqSample$Type) > 1){
+          for(i in 2:max(clonalFreqSample$Type)){
+              clonalFreqSampleType = clonalFreqSample[clonalFreqSample$Type == i,]
+              clonalityFrame.sub = clonalityFrame[clonalityFrame$clonaltype %in% clonalFreqSampleType$clonaltype,]
+              clonalityFrame.sub = clonalityFrame.sub[order(clonalityFrame.sub$clonaltype),]
+              write.table(clonalityFrame.sub, file=paste("coincidences_", sample, "_", i, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T)
+          }
+      }
+  }
+
+  clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
+  clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
+  clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
+  clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
+
+  ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
+  tcct = textConnection(ct)
+  CT  = read.table(tcct, sep="\t", header=TRUE)
+  close(tcct)
+  clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
+  clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
+
+  ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
+  ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
+  clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
+  ReplicateReads$Reads = as.numeric(ReplicateReads$Reads)
+  ReplicateReads$squared = as.numeric(ReplicateReads$Reads * ReplicateReads$Reads)
+
+  ReplicatePrint <- function(dat){
+      write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
   }
+
+  ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
+  lapply(ReplicateSplit, FUN=ReplicatePrint)
+
+  ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
+  clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
+
+  ReplicateSumPrint <- function(dat){
+      write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
+  }
+
+  ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
+  lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
+
+  clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
+  clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
+  clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
+  clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
+  clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
+
+  ClonalityScorePrint <- function(dat){
+      write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
+  }
+
+  clonalityScore = clonalFreqCount[c("Sample", "Result")]
+  clonalityScore = unique(clonalityScore)
+
+  clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
+  lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
+
+  clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
+
+
+
+  ClonalityOverviewPrint <- function(dat){
+      dat = dat[order(dat[,2]),]
+      write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".txt", sep=""), sep="\t",quote=F,na="-",row.names=F,col.names=F)
+  }
+
+  clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
+  lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
+  
 }
 
 bak = PRODF
@@ -724,16 +724,14 @@
   
   PRODF.with.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) > 2,]
   PRODF.no.D = PRODF[nchar(PRODF$Top.D.Gene, keepNA=F) < 4,]
+  write.table(PRODF.no.D, "productive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
   
   UNPROD.with.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) > 2,]
   UNPROD.no.D = UNPROD[nchar(UNPROD$Top.D.Gene, keepNA=F) < 4,]
+  write.table(UNPROD.no.D, "unproductive_no_D.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
   
   num_median = function(x, na.rm=T) { as.numeric(median(x, na.rm=na.rm)) }
 
-  print("---- table prod.with.d cdr3.length ----")
-  print(table(PRODF.with.D$CDR3.Length, useNA="ifany"))
-  print(median(PRODF.with.D$CDR3.Length, na.rm=T))
-
   newData = data.frame(data.table(PRODF.with.D)[,list(unique=.N, 
                                                VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                P1=mean(.SD$P3V.nt.nb, na.rm=T),
@@ -772,10 +770,6 @@
   newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
   write.table(newData, "junctionAnalysisProd_median_wD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
   
-  print("---- table unprod.with.d cdr3.length ----")
-  print(table(UNPROD.with.D$CDR3.Length, useNA="ifany"))
-  print(median(UNPROD.with.D$CDR3.Length, na.rm=T))
-  
   newData = data.frame(data.table(UNPROD.with.D)[,list(unique=.N, 
                                                 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                 P1=mean(.SD$P3V.nt.nb, na.rm=T),
@@ -819,11 +813,11 @@
   newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N, 
                                                VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                P1=mean(.SD$P3V.nt.nb, na.rm=T),
-                                               N1=mean(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                               N1=mean(.SD$N.REGION.nt.nb, na.rm=T),
                                                P2=mean(.SD$P5J.nt.nb, na.rm=T),
                                                DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
                                                Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
-                                               Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                               Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T),
                                                Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
                                                Median.CDR3.l=as.double(as.numeric(median(.SD$CDR3.Length, na.rm=T)))),
                                          by=c("Sample")])
@@ -833,39 +827,43 @@
   newData = data.frame(data.table(PRODF.no.D)[,list(unique=.N, 
                                                VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                P1=num_median(.SD$P3V.nt.nb, na.rm=T),
-                                               N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                               N1=median(.SD$N.REGION.nt.nb, na.rm=T),
                                                P2=num_median(.SD$P5J.nt.nb, na.rm=T),
                                                DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
 											   Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
-											   Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+											   Total.N=median(.SD$N.REGION.nt.nb, na.rm=T),
 											   Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
 											   Median.CDR3.l=as.double(as.numeric(median(.SD$CDR3.Length, na.rm=T)))),
                                          by=c("Sample")])
   newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
   write.table(newData, "junctionAnalysisProd_median_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
   
+  print(paste("mean N:", mean(UNPROD.no.D$N.REGION.nt.nb, na.rm=T)))
+  
   newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N, 
                                                 VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                 P1=mean(.SD$P3V.nt.nb, na.rm=T),
-                                                N1=mean(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                                N1=mean(.SD$N.REGION.nt.nb, na.rm=T),
                                                 P2=mean(.SD$P5J.nt.nb, na.rm=T),
                                                 DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
                                                 Total.Del=mean(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
-                                                Total.N=mean(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                                Total.N=mean(.SD$N.REGION.nt.nb, na.rm=T),
                                                 Total.P=mean(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
                                                 Median.CDR3.l=as.double(as.numeric(median(.SD$CDR3.Length, na.rm=T)))),
                                           by=c("Sample")])
   newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
   write.table(newData, "junctionAnalysisUnProd_mean_nD.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=F)
   
+  print(paste("median N:", num_median(UNPROD.no.D$N.REGION.nt.nb, na.rm=T)))
+  
     newData = data.frame(data.table(UNPROD.no.D)[,list(unique=.N, 
                                                 VH.DEL=num_median(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
                                                 P1=num_median(.SD$P3V.nt.nb, na.rm=T),
-                                                N1=num_median(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                                N1=median(.SD$N.REGION.nt.nb, na.rm=T),
                                                 P2=num_median(.SD$P5J.nt.nb, na.rm=T),
                                                 DEL.JH=num_median(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
                                                 Total.Del=num_median(rowSums(.SD[,c("X3V.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb"), with=F], na.rm=T)),
-                                                Total.N=num_median(rowSums(.SD[,c("N.REGION.nt.nb"), with=F], na.rm=T)),
+                                                Total.N=median(.SD$N.REGION.nt.nb, na.rm=T),
                                                 Total.P=num_median(rowSums(.SD[,c("P3V.nt.nb", "P5J.nt.nb"), with=F], na.rm=T)),
                                                 Median.CDR3.l=as.double(as.numeric(median(.SD$CDR3.Length, na.rm=T)))),
 															by=c("Sample")])
@@ -960,20 +958,13 @@
 clonaltype.in.replicates$clonaltype = do.call(paste, c(clonaltype.in.replicates[clonaltype], sep = ":"))
 clonaltype.in.replicates = clonaltype.in.replicates[,c("clonaltype","Replicate", "ID", "Sequence", "Sample")]
 
-print(head(clonaltype.in.replicates))
-
 clonaltype.counts = data.frame(table(clonaltype.in.replicates$clonaltype))
 names(clonaltype.counts) = c("clonaltype", "coincidence")
 
-print(head(clonaltype.counts))
-
 clonaltype.counts = clonaltype.counts[clonaltype.counts$coincidence > 1,]
 
-head(clonaltype.counts)
-
 clonaltype.in.replicates = clonaltype.in.replicates[clonaltype.in.replicates$clonaltype %in% clonaltype.counts$clonaltype,]
 clonaltype.in.replicates = merge(clonaltype.in.replicates, clonaltype.counts, by="clonaltype")
-print(head(clonaltype.in.replicates))
 clonaltype.in.replicates = clonaltype.in.replicates[order(clonaltype.in.replicates$clonaltype),c("coincidence","clonaltype", "Sample", "Replicate", "ID", "Sequence")]
 
 write.table(clonaltype.in.replicates, "clonaltypes_replicates.txt" , sep="\t",quote=F,na="-",row.names=F,col.names=T)
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/RScript.r~
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/report_clonality/RScript.r~	Mon Feb 13 09:08:46 2017 -0500
@@ -0,0 +1,658 @@
+# ---------------------- load/install packages ----------------------
+
+if (!("gridExtra" %in% rownames(installed.packages()))) {
+  install.packages("gridExtra", repos="http://cran.xl-mirror.nl/") 
+}
+library(gridExtra)
+if (!("ggplot2" %in% rownames(installed.packages()))) {
+  install.packages("ggplot2", repos="http://cran.xl-mirror.nl/") 
+}
+library(ggplot2)
+if (!("plyr" %in% rownames(installed.packages()))) {
+  install.packages("plyr", repos="http://cran.xl-mirror.nl/") 
+}			
+library(plyr)
+
+if (!("data.table" %in% rownames(installed.packages()))) {
+  install.packages("data.table", repos="http://cran.xl-mirror.nl/") 
+}
+library(data.table)
+
+if (!("reshape2" %in% rownames(installed.packages()))) {
+  install.packages("reshape2", repos="http://cran.xl-mirror.nl/") 
+}
+library(reshape2)
+
+if (!("lymphclon" %in% rownames(installed.packages()))) {
+  install.packages("lymphclon", repos="http://cran.xl-mirror.nl/") 
+}
+library(lymphclon)
+
+# ---------------------- parameters ----------------------
+
+args <- commandArgs(trailingOnly = TRUE)
+
+infile = args[1] #path to input file
+outfile = args[2] #path to output file
+outdir = args[3] #path to output folder (html/images/data)
+clonaltype = args[4] #clonaltype definition, or 'none' for no unique filtering
+ct = unlist(strsplit(clonaltype, ","))
+species = args[5] #human or mouse
+locus = args[6] # IGH, IGK, IGL, TRB, TRA, TRG or TRD
+filterproductive = ifelse(args[7] == "yes", T, F) #should unproductive sequences be filtered out? (yes/no)
+clonality_method = args[8]
+
+# ---------------------- Data preperation ----------------------
+
+inputdata = read.table(infile, sep="\t", header=TRUE, fill=T, comment.char="")
+
+setwd(outdir)
+
+# remove weird rows
+inputdata = inputdata[inputdata$Sample != "",]
+
+#remove the allele from the V,D and J genes
+inputdata$Top.V.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.V.Gene)
+inputdata$Top.D.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.D.Gene)
+inputdata$Top.J.Gene = gsub("[*]([0-9]+)", "", inputdata$Top.J.Gene)
+
+inputdata$clonaltype = 1:nrow(inputdata)
+
+PRODF = inputdata
+UNPROD = inputdata
+if(filterproductive){
+  if("Functionality" %in% colnames(inputdata)) { # "Functionality" is an IMGT column
+    PRODF = inputdata[inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)", ]
+    UNPROD = inputdata[!(inputdata$Functionality == "productive" | inputdata$Functionality == "productive (see comment)"), ]
+  } else {
+    PRODF = inputdata[inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" , ]
+    UNPROD = inputdata[!(inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND" ), ]
+  }
+}
+
+clonalityFrame = PRODF
+
+#remove duplicates based on the clonaltype
+if(clonaltype != "none"){
+  clonaltype = paste(clonaltype, ",Sample", sep="") #add sample column to clonaltype, unique within samples
+  PRODF$clonaltype = do.call(paste, c(PRODF[unlist(strsplit(clonaltype, ","))], sep = ":"))
+  PRODF = PRODF[!duplicated(PRODF$clonaltype), ]
+  
+  UNPROD$clonaltype = do.call(paste, c(UNPROD[unlist(strsplit(clonaltype, ","))], sep = ":"))
+  UNPROD = UNPROD[!duplicated(UNPROD$clonaltype), ]
+  
+  #again for clonalityFrame but with sample+replicate
+  clonalityFrame$clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(clonaltype, ","))], sep = ":"))
+  clonalityFrame$clonality_clonaltype = do.call(paste, c(clonalityFrame[unlist(strsplit(paste(clonaltype, ",Replicate", sep=""), ","))], sep = ":"))
+  clonalityFrame = clonalityFrame[!duplicated(clonalityFrame$clonality_clonaltype), ]
+}
+
+PRODF$freq = 1
+
+if(any(grepl(pattern="_", x=PRODF$ID))){ #the frequency can be stored in the ID with the pattern ".*_freq_.*"
+  PRODF$freq = gsub("^[0-9]+_", "", PRODF$ID)
+  PRODF$freq = gsub("_.*", "", PRODF$freq)
+  PRODF$freq = as.numeric(PRODF$freq)
+  if(any(is.na(PRODF$freq))){ #if there was an "_" in the ID, but not the frequency, go back to frequency of 1 for every sequence
+    PRODF$freq = 1
+  }
+}
+
+
+
+#write the complete dataset that is left over, will be the input if 'none' for clonaltype and 'no' for filterproductive
+write.table(PRODF, "allUnique.txt", sep=",",quote=F,row.names=F,col.names=T)
+write.table(PRODF, "allUnique.csv", sep="\t",quote=F,row.names=F,col.names=T)
+write.table(UNPROD, "allUnproductive.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+#write the samples to a file
+sampleFile <- file("samples.txt")
+un = unique(inputdata$Sample)
+un = paste(un, sep="\n")
+writeLines(un, sampleFile)
+close(sampleFile)
+
+# ---------------------- Counting the productive/unproductive and unique sequences ----------------------
+
+if(!("Functionality" %in% inputdata)){ #add a functionality column to the igblast data
+  inputdata$Functionality = "unproductive"
+  search = (inputdata$VDJ.Frame != "In-frame with stop codon" & inputdata$VDJ.Frame != "Out-of-frame" & inputdata$CDR3.Found.How != "NOT_FOUND")
+  if(sum(search) > 0){
+    inputdata[search,]$Functionality = "productive"
+  }
+}
+
+inputdata.dt = data.table(inputdata) #for speed
+
+if(clonaltype == "none"){
+  ct = c("clonaltype")
+}
+
+inputdata.dt$samples_replicates = paste(inputdata.dt$Sample, inputdata.dt$Replicate, sep="_")
+samples_replicates = c(unique(inputdata.dt$samples_replicates), unique(as.character(inputdata.dt$Sample)))
+frequency_table = data.frame(ID = samples_replicates[order(samples_replicates)])
+
+
+sample_productive_count = inputdata.dt[, list(All=.N, 
+                                              Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), 
+                                              perc_prod = 1,
+                                              Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), 
+                                              perc_prod_un = 1,
+                                              Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
+                                              perc_unprod = 1,
+                                              Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
+                                              perc_unprod_un = 1),
+                                       by=c("Sample")]
+
+sample_productive_count$perc_prod = round(sample_productive_count$Productive / sample_productive_count$All * 100)
+sample_productive_count$perc_prod_un = round(sample_productive_count$Productive_unique / sample_productive_count$All * 100)
+
+sample_productive_count$perc_unprod = round(sample_productive_count$Unproductive / sample_productive_count$All * 100)
+sample_productive_count$perc_unprod_un = round(sample_productive_count$Unproductive_unique / sample_productive_count$All * 100)
+
+
+sample_replicate_productive_count = inputdata.dt[, list(All=.N, 
+                                                        Productive = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",]), 
+                                                        perc_prod = 1,
+                                                        Productive_unique = nrow(.SD[.SD$Functionality == "productive" | .SD$Functionality == "productive (see comment)",list(count=.N),by=ct]), 
+                                                        perc_prod_un = 1,
+                                                        Unproductive= nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",]),
+                                                        perc_unprod = 1,
+                                                        Unproductive_unique =nrow(.SD[.SD$Functionality != "productive" & .SD$Functionality != "productive (see comment)",list(count=.N),by=ct]),
+                                                        perc_unprod_un = 1),
+                                                 by=c("samples_replicates")]
+
+sample_replicate_productive_count$perc_prod = round(sample_replicate_productive_count$Productive / sample_replicate_productive_count$All * 100)
+sample_replicate_productive_count$perc_prod_un = round(sample_replicate_productive_count$Productive_unique / sample_replicate_productive_count$All * 100)
+
+sample_replicate_productive_count$perc_unprod = round(sample_replicate_productive_count$Unproductive / sample_replicate_productive_count$All * 100)
+sample_replicate_productive_count$perc_unprod_un = round(sample_replicate_productive_count$Unproductive_unique / sample_replicate_productive_count$All * 100)
+
+setnames(sample_replicate_productive_count, colnames(sample_productive_count))
+
+counts = rbind(sample_replicate_productive_count, sample_productive_count)
+counts = counts[order(counts$Sample),]
+
+write.table(x=counts, file="productive_counting.txt", sep=",",quote=F,row.names=F,col.names=F)
+
+# ---------------------- Frequency calculation for V, D and J ----------------------
+
+PRODFV = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.V.Gene")])
+Total = ddply(PRODFV, .(Sample), function(x) data.frame(Total = sum(x$Length)))
+PRODFV = merge(PRODFV, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
+PRODFV = ddply(PRODFV, c("Sample", "Top.V.Gene"), summarise, relFreq= (Length*100 / Total))
+
+PRODFD = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.D.Gene")])
+Total = ddply(PRODFD, .(Sample), function(x) data.frame(Total = sum(x$Length)))
+PRODFD = merge(PRODFD, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
+PRODFD = ddply(PRODFD, c("Sample", "Top.D.Gene"), summarise, relFreq= (Length*100 / Total))
+
+PRODFJ = data.frame(data.table(PRODF)[, list(Length=sum(freq)), by=c("Sample", "Top.J.Gene")])
+Total = ddply(PRODFJ, .(Sample), function(x) data.frame(Total = sum(x$Length)))
+PRODFJ = merge(PRODFJ, Total, by.x='Sample', by.y='Sample', all.x=TRUE)
+PRODFJ = ddply(PRODFJ, c("Sample", "Top.J.Gene"), summarise, relFreq= (Length*100 / Total))
+
+# ---------------------- Setting up the gene names for the different species/loci ----------------------
+
+Vchain = ""
+Dchain = ""
+Jchain = ""
+
+if(species == "custom"){
+	print("Custom genes: ")
+	splt = unlist(strsplit(locus, ";"))
+	print(paste("V:", splt[1]))
+	print(paste("D:", splt[2]))
+	print(paste("J:", splt[3]))
+	
+	Vchain = unlist(strsplit(splt[1], ","))
+	Vchain = data.frame(v.name = Vchain, chr.orderV = 1:length(Vchain))
+	
+	Dchain = unlist(strsplit(splt[2], ","))
+	if(length(Dchain) > 0){
+		Dchain = data.frame(v.name = Dchain, chr.orderD = 1:length(Dchain))
+	} else {
+		Dchain = data.frame(v.name = character(0), chr.orderD = numeric(0))
+	}
+	
+	Jchain = unlist(strsplit(splt[3], ","))
+	Jchain = data.frame(v.name = Jchain, chr.orderJ = 1:length(Jchain))
+
+} else {
+	genes = read.table("genes.txt", sep="\t", header=TRUE, fill=T, comment.char="")
+
+	Vchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "V",c("IMGT.GENE.DB", "chr.order")]
+	colnames(Vchain) = c("v.name", "chr.orderV")
+	Dchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "D",c("IMGT.GENE.DB", "chr.order")]
+	colnames(Dchain) = c("v.name", "chr.orderD")
+	Jchain = genes[grepl(species, genes$Species) & genes$locus == locus & genes$region == "J",c("IMGT.GENE.DB", "chr.order")]
+	colnames(Jchain) = c("v.name", "chr.orderJ")
+}
+useD = TRUE
+if(nrow(Dchain) == 0){
+  useD = FALSE
+  cat("No D Genes in this species/locus")
+}
+print(paste("useD:", useD))
+
+# ---------------------- merge with the frequency count ----------------------
+
+PRODFV = merge(PRODFV, Vchain, by.x='Top.V.Gene', by.y='v.name', all.x=TRUE)
+
+PRODFD = merge(PRODFD, Dchain, by.x='Top.D.Gene', by.y='v.name', all.x=TRUE)
+
+PRODFJ = merge(PRODFJ, Jchain, by.x='Top.J.Gene', by.y='v.name', all.x=TRUE)
+
+# ---------------------- Create the V, D and J frequency plots and write the data.frame for every plot to a file ----------------------
+
+pV = ggplot(PRODFV)
+pV = pV + geom_bar( aes( x=factor(reorder(Top.V.Gene, chr.orderV)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
+pV = pV + xlab("Summary of V gene") + ylab("Frequency") + ggtitle("Relative frequency of V gene usage")
+write.table(x=PRODFV, file="VFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+png("VPlot.png",width = 1280, height = 720)
+pV
+dev.off();
+
+if(useD){
+  pD = ggplot(PRODFD)
+  pD = pD + geom_bar( aes( x=factor(reorder(Top.D.Gene, chr.orderD)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
+  pD = pD + xlab("Summary of D gene") + ylab("Frequency") + ggtitle("Relative frequency of D gene usage")
+  write.table(x=PRODFD, file="DFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+  
+  png("DPlot.png",width = 800, height = 600)
+  print(pD)
+  dev.off();
+}
+
+pJ = ggplot(PRODFJ)
+pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
+pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
+write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+png("JPlot.png",width = 800, height = 600)
+pJ
+dev.off();
+
+pJ = ggplot(PRODFJ)
+pJ = pJ + geom_bar( aes( x=factor(reorder(Top.J.Gene, chr.orderJ)), y=relFreq, fill=Sample), stat='identity', position="dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1))
+pJ = pJ + xlab("Summary of J gene") + ylab("Frequency") + ggtitle("Relative frequency of J gene usage")
+write.table(x=PRODFJ, file="JFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+png("JPlot.png",width = 800, height = 600)
+pJ
+dev.off();
+
+# ---------------------- Now the frequency plots of the V, D and J families ----------------------
+
+VGenes = PRODF[,c("Sample", "Top.V.Gene")]
+VGenes$Top.V.Gene = gsub("-.*", "", VGenes$Top.V.Gene)
+VGenes = data.frame(data.table(VGenes)[, list(Count=.N), by=c("Sample", "Top.V.Gene")])
+TotalPerSample = data.frame(data.table(VGenes)[, list(total=sum(.SD$Count)), by=Sample])
+VGenes = merge(VGenes, TotalPerSample, by="Sample")
+VGenes$Frequency = VGenes$Count * 100 / VGenes$total
+VPlot = ggplot(VGenes)
+VPlot = VPlot + geom_bar(aes( x = Top.V.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+  ggtitle("Distribution of V gene families") + 
+  ylab("Percentage of sequences")
+png("VFPlot.png")
+VPlot
+dev.off();
+write.table(x=VGenes, file="VFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+if(useD){
+  DGenes = PRODF[,c("Sample", "Top.D.Gene")]
+  DGenes$Top.D.Gene = gsub("-.*", "", DGenes$Top.D.Gene)
+  DGenes = data.frame(data.table(DGenes)[, list(Count=.N), by=c("Sample", "Top.D.Gene")])
+  TotalPerSample = data.frame(data.table(DGenes)[, list(total=sum(.SD$Count)), by=Sample])
+  DGenes = merge(DGenes, TotalPerSample, by="Sample")
+  DGenes$Frequency = DGenes$Count * 100 / DGenes$total
+  DPlot = ggplot(DGenes)
+  DPlot = DPlot + geom_bar(aes( x = Top.D.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+    ggtitle("Distribution of D gene families") + 
+    ylab("Percentage of sequences")
+  png("DFPlot.png")
+  print(DPlot)
+  dev.off();
+  write.table(x=DGenes, file="DFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+}
+
+JGenes = PRODF[,c("Sample", "Top.J.Gene")]
+JGenes$Top.J.Gene = gsub("-.*", "", JGenes$Top.J.Gene)
+JGenes = data.frame(data.table(JGenes)[, list(Count=.N), by=c("Sample", "Top.J.Gene")])
+TotalPerSample = data.frame(data.table(JGenes)[, list(total=sum(.SD$Count)), by=Sample])
+JGenes = merge(JGenes, TotalPerSample, by="Sample")
+JGenes$Frequency = JGenes$Count * 100 / JGenes$total
+JPlot = ggplot(JGenes)
+JPlot = JPlot + geom_bar(aes( x = Top.J.Gene, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+  ggtitle("Distribution of J gene families") + 
+  ylab("Percentage of sequences")
+png("JFPlot.png")
+JPlot
+dev.off();
+write.table(x=JGenes, file="JFFrequency.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+# ---------------------- Plotting the cdr3 length ----------------------
+
+CDR3Length = data.frame(data.table(PRODF)[, list(Count=.N), by=c("Sample", "CDR3.Length.DNA")])
+TotalPerSample = data.frame(data.table(CDR3Length)[, list(total=sum(.SD$Count)), by=Sample])
+CDR3Length = merge(CDR3Length, TotalPerSample, by="Sample")
+CDR3Length$Frequency = CDR3Length$Count * 100 / CDR3Length$total
+CDR3LengthPlot = ggplot(CDR3Length)
+CDR3LengthPlot = CDR3LengthPlot + geom_bar(aes( x = CDR3.Length.DNA, y = Frequency, fill = Sample), stat='identity', position='dodge' ) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+  ggtitle("Length distribution of CDR3") + 
+  xlab("CDR3 Length") + 
+  ylab("Percentage of sequences")
+png("CDR3LengthPlot.png",width = 1280, height = 720)
+CDR3LengthPlot
+dev.off()
+write.table(x=CDR3Length, file="CDR3LengthPlot.csv", sep=",",quote=F,row.names=F,col.names=T)
+
+# ---------------------- Plot the heatmaps ----------------------
+
+
+#get the reverse order for the V and D genes
+revVchain = Vchain
+revDchain = Dchain
+revVchain$chr.orderV = rev(revVchain$chr.orderV)
+revDchain$chr.orderD = rev(revDchain$chr.orderD)
+
+if(useD){
+  plotVD <- function(dat){
+    if(length(dat[,1]) == 0){
+      return()
+    }
+    img = ggplot() + 
+      geom_tile(data=dat, aes(x=factor(reorder(Top.D.Gene, chr.orderD)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + 
+      theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+      scale_fill_gradient(low="gold", high="blue", na.value="white") + 
+      ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + 
+      xlab("D genes") + 
+      ylab("V Genes")
+    
+    png(paste("HeatmapVD_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Dchain$v.name)), height=100+(15*length(Vchain$v.name)))
+    print(img)
+    dev.off()
+    write.table(x=acast(dat, Top.V.Gene~Top.D.Gene, value.var="Length"), file=paste("HeatmapVD_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
+  }
+  
+  VandDCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.D.Gene", "Sample")])
+  
+  VandDCount$l = log(VandDCount$Length)
+  maxVD = data.frame(data.table(VandDCount)[, list(max=max(l)), by=c("Sample")])
+  VandDCount = merge(VandDCount, maxVD, by.x="Sample", by.y="Sample", all.x=T)
+  VandDCount$relLength = VandDCount$l / VandDCount$max
+  
+  cartegianProductVD = expand.grid(Top.V.Gene = Vchain$v.name, Top.D.Gene = Dchain$v.name, Sample = unique(inputdata$Sample))
+  
+  completeVD = merge(VandDCount, cartegianProductVD, all.y=TRUE)
+  completeVD = merge(completeVD, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
+  completeVD = merge(completeVD, Dchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
+  VDList = split(completeVD, f=completeVD[,"Sample"])
+  
+  lapply(VDList, FUN=plotVD)
+}
+
+plotVJ <- function(dat){
+  if(length(dat[,1]) == 0){
+    return()
+  }
+  cat(paste(unique(dat[3])[1,1]))
+  img = ggplot() + 
+    geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.V.Gene, chr.orderV)), fill=relLength)) + 
+    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+    scale_fill_gradient(low="gold", high="blue", na.value="white") + 
+    ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + 
+    xlab("J genes") + 
+    ylab("V Genes")
+  
+  png(paste("HeatmapVJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Vchain$v.name)))
+  print(img)
+  dev.off()
+  write.table(x=acast(dat, Top.V.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapVJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
+}
+
+VandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.V.Gene", "Top.J.Gene", "Sample")])
+
+VandJCount$l = log(VandJCount$Length)
+maxVJ = data.frame(data.table(VandJCount)[, list(max=max(l)), by=c("Sample")])
+VandJCount = merge(VandJCount, maxVJ, by.x="Sample", by.y="Sample", all.x=T)
+VandJCount$relLength = VandJCount$l / VandJCount$max
+
+cartegianProductVJ = expand.grid(Top.V.Gene = Vchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
+
+completeVJ = merge(VandJCount, cartegianProductVJ, all.y=TRUE)
+completeVJ = merge(completeVJ, revVchain, by.x="Top.V.Gene", by.y="v.name", all.x=TRUE)
+completeVJ = merge(completeVJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
+VJList = split(completeVJ, f=completeVJ[,"Sample"])
+lapply(VJList, FUN=plotVJ)
+
+if(useD){
+  plotDJ <- function(dat){
+    if(length(dat[,1]) == 0){
+      return()
+    }
+    img = ggplot() + 
+      geom_tile(data=dat, aes(x=factor(reorder(Top.J.Gene, chr.orderJ)), y=factor(reorder(Top.D.Gene, chr.orderD)), fill=relLength)) + 
+      theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
+      scale_fill_gradient(low="gold", high="blue", na.value="white") + 
+      ggtitle(paste(unique(dat$Sample), " (N=" , sum(dat$Length, na.rm=T) ,")", sep="")) + 
+      xlab("J genes") + 
+      ylab("D Genes")
+    
+    png(paste("HeatmapDJ_", unique(dat[3])[1,1] , ".png", sep=""), width=150+(15*length(Jchain$v.name)), height=100+(15*length(Dchain$v.name)))
+    print(img)
+    dev.off()
+    write.table(x=acast(dat, Top.D.Gene~Top.J.Gene, value.var="Length"), file=paste("HeatmapDJ_", unique(dat[3])[1,1], ".csv", sep=""), sep=",",quote=F,row.names=T,col.names=NA)
+  }
+  
+  
+  DandJCount = data.frame(data.table(PRODF)[, list(Length=.N), by=c("Top.D.Gene", "Top.J.Gene", "Sample")])
+  
+  DandJCount$l = log(DandJCount$Length)
+  maxDJ = data.frame(data.table(DandJCount)[, list(max=max(l)), by=c("Sample")])
+  DandJCount = merge(DandJCount, maxDJ, by.x="Sample", by.y="Sample", all.x=T)
+  DandJCount$relLength = DandJCount$l / DandJCount$max
+  
+  cartegianProductDJ = expand.grid(Top.D.Gene = Dchain$v.name, Top.J.Gene = Jchain$v.name, Sample = unique(inputdata$Sample))
+  
+  completeDJ = merge(DandJCount, cartegianProductDJ, all.y=TRUE)
+  completeDJ = merge(completeDJ, revDchain, by.x="Top.D.Gene", by.y="v.name", all.x=TRUE)
+  completeDJ = merge(completeDJ, Jchain, by.x="Top.J.Gene", by.y="v.name", all.x=TRUE)
+  DJList = split(completeDJ, f=completeDJ[,"Sample"])
+  lapply(DJList, FUN=plotDJ)
+}
+
+
+# ---------------------- calculating the clonality score ----------------------
+
+if("Replicate" %in% colnames(inputdata)) #can only calculate clonality score when replicate information is available
+{
+  if(clonality_method == "boyd"){
+    samples = split(clonalityFrame, clonalityFrame$Sample, drop=T)
+   
+    for (sample in samples){
+      res = data.frame(paste=character(0))
+      sample_id = unique(sample$Sample)[[1]]
+      for(replicate in unique(sample$Replicate)){
+        tmp = sample[sample$Replicate == replicate,]
+        clone_table = data.frame(table(tmp$clonaltype))
+        clone_col_name = paste("V", replicate, sep="")
+        colnames(clone_table) = c("paste", clone_col_name)
+        res = merge(res, clone_table, by="paste", all=T)
+      }
+      
+      res[is.na(res)] = 0      
+      infer.result = infer.clonality(as.matrix(res[,2:ncol(res)]))
+      
+      write.table(data.table(infer.result[[12]]), file=paste("lymphclon_clonality_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=F)
+      
+      res$type = rowSums(res[,2:ncol(res)])
+      
+      coincidence.table = data.frame(table(res$type))
+      colnames(coincidence.table) = c("Coincidence Type",  "Raw Coincidence Freq")
+      write.table(coincidence.table, file=paste("lymphclon_coincidences_", sample_id, ".csv", sep=""), sep=",",quote=F,row.names=F,col.names=T)
+    }
+  } else {
+    write.table(clonalityFrame, "clonalityComplete.csv", sep=",",quote=F,row.names=F,col.names=T)
+      
+    clonalFreq = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "clonaltype")])
+    clonalFreqCount = data.frame(data.table(clonalFreq)[, list(Count=.N), by=c("Sample", "Type")])
+    clonalFreqCount$realCount = clonalFreqCount$Type * clonalFreqCount$Count
+    clonalSum = data.frame(data.table(clonalFreqCount)[, list(Reads=sum(realCount)), by=c("Sample")])
+    clonalFreqCount = merge(clonalFreqCount, clonalSum, by.x="Sample", by.y="Sample")
+    
+    ct = c('Type\tWeight\n2\t1\n3\t3\n4\t6\n5\t10\n6\t15')
+    tcct = textConnection(ct)
+    CT  = read.table(tcct, sep="\t", header=TRUE)
+    close(tcct)
+    clonalFreqCount = merge(clonalFreqCount, CT, by.x="Type", by.y="Type", all.x=T)
+    clonalFreqCount$WeightedCount = clonalFreqCount$Count * clonalFreqCount$Weight
+    
+    ReplicateReads = data.frame(data.table(clonalityFrame)[, list(Type=.N), by=c("Sample", "Replicate", "clonaltype")])
+    ReplicateReads = data.frame(data.table(ReplicateReads)[, list(Reads=.N), by=c("Sample", "Replicate")])
+    clonalFreqCount$Reads = as.numeric(clonalFreqCount$Reads)
+    ReplicateReads$squared = ReplicateReads$Reads * ReplicateReads$Reads
+    
+    ReplicatePrint <- function(dat){
+      write.table(dat[-1], paste("ReplicateReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
+    }
+    
+    ReplicateSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
+    lapply(ReplicateSplit, FUN=ReplicatePrint)
+    
+    ReplicateReads = data.frame(data.table(ReplicateReads)[, list(ReadsSum=sum(as.numeric(Reads)), ReadsSquaredSum=sum(as.numeric(squared))), by=c("Sample")])
+    clonalFreqCount = merge(clonalFreqCount, ReplicateReads, by.x="Sample", by.y="Sample", all.x=T)
+    
+    ReplicateSumPrint <- function(dat){
+      write.table(dat[-1], paste("ReplicateSumReads_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
+    }
+    
+    ReplicateSumSplit = split(ReplicateReads, f=ReplicateReads[,"Sample"])
+    lapply(ReplicateSumSplit, FUN=ReplicateSumPrint)
+    
+    clonalFreqCountSum = data.frame(data.table(clonalFreqCount)[, list(Numerator=sum(WeightedCount, na.rm=T)), by=c("Sample")])
+    clonalFreqCount = merge(clonalFreqCount, clonalFreqCountSum, by.x="Sample", by.y="Sample", all.x=T)
+    clonalFreqCount$ReadsSum = as.numeric(clonalFreqCount$ReadsSum) #prevent integer overflow
+    clonalFreqCount$Denominator = (((clonalFreqCount$ReadsSum * clonalFreqCount$ReadsSum) - clonalFreqCount$ReadsSquaredSum) / 2)
+    clonalFreqCount$Result = (clonalFreqCount$Numerator + 1) / (clonalFreqCount$Denominator + 1)
+    
+    ClonalityScorePrint <- function(dat){
+      write.table(dat$Result, paste("ClonalityScore_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
+    }
+    
+    clonalityScore = clonalFreqCount[c("Sample", "Result")]
+    clonalityScore = unique(clonalityScore)
+    
+    clonalityScoreSplit = split(clonalityScore, f=clonalityScore[,"Sample"])
+    lapply(clonalityScoreSplit, FUN=ClonalityScorePrint)
+    
+    clonalityOverview = clonalFreqCount[c("Sample", "Type", "Count", "Weight", "WeightedCount")]
+    
+    
+    
+    ClonalityOverviewPrint <- function(dat){
+      write.table(dat[-1], paste("ClonalityOverView_", unique(dat[1])[1,1] , ".csv", sep=""), sep=",",quote=F,na="-",row.names=F,col.names=F)
+    }
+    
+    clonalityOverviewSplit = split(clonalityOverview, f=clonalityOverview$Sample)
+    lapply(clonalityOverviewSplit, FUN=ClonalityOverviewPrint)
+  }
+}
+
+imgtcolumns = c("X3V.REGION.trimmed.nt.nb","P3V.nt.nb", "N1.REGION.nt.nb", "P5D.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "P3D.nt.nb", "N2.REGION.nt.nb", "P5J.nt.nb", "X5J.REGION.trimmed.nt.nb", "X3V.REGION.trimmed.nt.nb", "X5D.REGION.trimmed.nt.nb", "X3D.REGION.trimmed.nt.nb", "X5J.REGION.trimmed.nt.nb", "N1.REGION.nt.nb", "N2.REGION.nt.nb", "P3V.nt.nb", "P5D.nt.nb", "P3D.nt.nb", "P5J.nt.nb")
+if(all(imgtcolumns %in% colnames(inputdata)))
+{
+  print("found IMGT columns, running junction analysis")
+  newData = data.frame(data.table(PRODF)[,list(unique=.N, 
+                                               VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
+                                               P1=mean(.SD$P3V.nt.nb, na.rm=T),
+                                               N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
+                                               P2=mean(.SD$P5D.nt.nb, na.rm=T),
+                                               DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
+                                               DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
+                                               P3=mean(.SD$P3D.nt.nb, na.rm=T),
+                                               N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
+                                               P4=mean(.SD$P5J.nt.nb, na.rm=T),
+                                               DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
+                                               Total.Del=(	mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) + 
+                                                             mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) + 
+                                                             mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
+                                                             mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
+                                               
+                                               Total.N=(	mean(.SD$N1.REGION.nt.nb, na.rm=T) +
+                                                           mean(.SD$N2.REGION.nt.nb, na.rm=T)),
+                                               
+                                               Total.P=(	mean(.SD$P3V.nt.nb, na.rm=T) +
+                                                           mean(.SD$P5D.nt.nb, na.rm=T) +
+                                                           mean(.SD$P3D.nt.nb, na.rm=T) +
+                                                           mean(.SD$P5J.nt.nb, na.rm=T))),
+                                         by=c("Sample")])
+  print(newData)
+  newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
+  write.table(newData, "junctionAnalysisProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
+  
+  newData = data.frame(data.table(UNPROD)[,list(unique=.N, 
+                                                VH.DEL=mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T),
+                                                P1=mean(.SD$P3V.nt.nb, na.rm=T),
+                                                N1=mean(.SD$N1.REGION.nt.nb, na.rm=T),
+                                                P2=mean(.SD$P5D.nt.nb, na.rm=T),
+                                                DEL.DH=mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T),
+                                                DH.DEL=mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T),
+                                                P3=mean(.SD$P3D.nt.nb, na.rm=T),
+                                                N2=mean(.SD$N2.REGION.nt.nb, na.rm=T),
+                                                P4=mean(.SD$P5J.nt.nb, na.rm=T),
+                                                DEL.JH=mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T),
+                                                Total.Del=(mean(.SD$X3V.REGION.trimmed.nt.nb, na.rm=T) + 
+                                                           mean(.SD$X5D.REGION.trimmed.nt.nb, na.rm=T) + 
+                                                           mean(.SD$X3D.REGION.trimmed.nt.nb, na.rm=T) +
+                                                           mean(.SD$X5J.REGION.trimmed.nt.nb, na.rm=T)),
+                                                Total.N=(  mean(.SD$N1.REGION.nt.nb, na.rm=T) +
+                                                           mean(.SD$N2.REGION.nt.nb, na.rm=T)),
+                                                Total.P=(  mean(.SD$P3V.nt.nb, na.rm=T) +
+							   mean(.SD$P5D.nt.nb, na.rm=T) +
+							   mean(.SD$P3D.nt.nb, na.rm=T) +
+						           mean(.SD$P5J.nt.nb, na.rm=T))),
+                                          by=c("Sample")])
+  newData[,sapply(newData, is.numeric)] = round(newData[,sapply(newData, is.numeric)],1)
+  write.table(newData, "junctionAnalysisUnProd.csv" , sep=",",quote=F,na="-",row.names=F,col.names=F)
+}
+
+# ---------------------- AA composition in CDR3 ----------------------
+
+AACDR3 = PRODF[,c("Sample", "CDR3.Seq")]
+
+TotalPerSample = data.frame(data.table(AACDR3)[, list(total=sum(nchar(as.character(.SD$CDR3.Seq)))), by=Sample])
+
+AAfreq = list()
+
+for(i in 1:nrow(TotalPerSample)){
+	sample = TotalPerSample$Sample[i]
+  AAfreq[[i]] = data.frame(table(unlist(strsplit(as.character(AACDR3[AACDR3$Sample == sample,c("CDR3.Seq")]), ""))))
+  AAfreq[[i]]$Sample = sample
+}
+
+AAfreq = ldply(AAfreq, data.frame)
+AAfreq = merge(AAfreq, TotalPerSample, by="Sample", all.x = T)
+AAfreq$freq_perc = as.numeric(AAfreq$Freq / AAfreq$total * 100)
+
+
+AAorder = read.table(sep="\t", header=TRUE, text="order.aa\tAA\n1\tR\n2\tK\n3\tN\n4\tD\n5\tQ\n6\tE\n7\tH\n8\tP\n9\tY\n10\tW\n11\tS\n12\tT\n13\tG\n14\tA\n15\tM\n16\tC\n17\tF\n18\tL\n19\tV\n20\tI")
+AAfreq = merge(AAfreq, AAorder, by.x='Var1', by.y='AA', all.x=TRUE)
+
+AAfreq = AAfreq[!is.na(AAfreq$order.aa),]
+
+AAfreqplot = ggplot(AAfreq)
+AAfreqplot = AAfreqplot + geom_bar(aes( x=factor(reorder(Var1, order.aa)), y = freq_perc, fill = Sample), stat='identity', position='dodge' )
+AAfreqplot = AAfreqplot + annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
+AAfreqplot = AAfreqplot + annotate("rect", xmin = 3.5, xmax = 4.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
+AAfreqplot = AAfreqplot + annotate("rect", xmin = 5.5, xmax = 6.5, ymin = 0, ymax = Inf, fill = "blue", alpha = 0.2)
+AAfreqplot = AAfreqplot + annotate("rect", xmin = 6.5, xmax = 7.5, ymin = 0, ymax = Inf, fill = "red", alpha = 0.2)
+AAfreqplot = AAfreqplot + ggtitle("Amino Acid Composition in the CDR3") + xlab("Amino Acid, from Hydrophilic (left) to Hydrophobic (right)") + ylab("Percentage")
+
+png("AAComposition.png",width = 1280, height = 720)
+AAfreqplot
+dev.off()
+write.table(AAfreq, "AAComposition.csv" , sep=",",quote=F,na="-",row.names=F,col.names=T)
+
+
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/circos/fonts.conf~
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/report_clonality/circos/fonts.conf~	Mon Feb 13 09:08:46 2017 -0500
@@ -0,0 +1,8 @@
+
+default       = LTe50046.ttf
+normal        = LTe50046.ttf
+bold          = LTe50048.ttf
+condensed     = LTe50050.ttf
+condensedbold = LTe50054.ttf
+mono          = pragmata.ttf
+glyph         = wingding.ttf
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/circos/housekeeping.conf~
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/r_wrapper.sh
--- a/report_clonality/r_wrapper.sh	Thu Feb 09 07:20:09 2017 -0500
+++ b/report_clonality/r_wrapper.sh	Mon Feb 13 09:08:46 2017 -0500
@@ -222,54 +222,40 @@
 	
 	for sample in $samples; do
 		echo "${clonality_method}"
-		if [[ "${clonality_method}" == "old" ]] ; then
-			echo "in old"
-			clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)"
-			echo "<div class='tabbertab' title='$sample'><table class='pure-table pure-table-striped'>" >> $outputFile
-			
-			if [[ "${clonality_method}" == "boyd" ]] ; then
-				echo "<thead><tr><th colspan='2'>Clonality Score: $clonalityScore</th></tr></thead>" >> $outputFile
-			fi
-
-			#replicate,reads,squared
-			echo "<tr><td>Replicate ID</td><td>Number of Sequences</td></tr>" >> $outputFile
-			while IFS=, read replicate reads squared
-			do
-				echo "<tr><td>$replicate</td><td>$reads</td></tr>" >> $outputFile
-			done < $outputDir/ReplicateReads_$sample.csv
-			
-			#sum of reads and reads squared
-			while IFS=, read readsSum squaredSum
-				do
-					echo "<tr><td>Sum</td><td>$readsSum</td></tr>" >> $outputFile
-			done < $outputDir/ReplicateSumReads_$sample.csv
-			
-			echo "<tr><td></td><td></td></tr>" >> $outputFile
-			
-			#overview
-			echo "<tr><td>Number of replicates containing the coincidence</td><td>Number of sequences shared between replicates</td></tr>" >> $outputFile
-			while IFS=, read type count weight weightedCount
-			do
-				if [[ "$type" -eq "1" ]]; then
-					echo "<tr><td>$type</td><td>$count</td></tr>" >> $outputFile
-				else
-					echo "<tr><td><a href='coincidences_${sample}_${type}.txt'>$type</a></td><td>$count</td></tr>" >> $outputFile
-				fi
-				
-			done < $outputDir/ClonalityOverView_$sample.csv
-			echo "</table></div>" >> $outputFile
-		else
-			echo "in new"
+		
+		echo "<div class='tabbertab' title='$sample'><table class='pure-table pure-table-striped'>" >> $outputFile
+		
+		if [[ "${clonality_method}" == "boyd" ]] ; then
 			clonalityScore="$(cat $outputDir/lymphclon_clonality_${sample}.txt)"
-			echo "<div class='tabbertab' title='$sample'>" >> $outputFile
-			echo "Lymphclon clonality score: <br />$clonalityScore<br /><br />" >> $outputFile
-			echo "<table border = 1>" >> $outputFile
-			while read type count
-			do
-				echo "<tr><td>$type</td><td>$count</td></tr>" >> $outputFile
-			done < $outputDir/lymphclon_coincidences_$sample.txt
-			echo "</table></div>" >> $outputFile
+            echo "<tr><td>Clonality Score: </td><td>$clonalityScore</td></tr>" >> $outputFile
 		fi
+		
+		#replicate,reads,squared
+        echo "<tr><td>Replicate ID</td><td>Number of Sequences</td></tr>" >> $outputFile
+        while read replicate reads squared
+        do
+            echo "<tr><td>$replicate</td><td>$reads</td></tr>" >> $outputFile
+        done < $outputDir/ReplicateReads_$sample.txt
+        
+        #sum of reads and reads squared
+        while read readsSum squaredSum
+            do
+                echo "<tr><td>Sum</td><td>$readsSum</td></tr>" >> $outputFile
+        done < $outputDir/ReplicateSumReads_$sample.txt
+        
+        echo "<tr><td></td><td></td></tr>" >> $outputFile
+        
+        #overview
+        echo "<tr><td>Number of replicates containing the coincidence</td><td>Number of sequences shared between replicates</td></tr>" >> $outputFile
+        while read type count weight weightedCount
+        do
+            if [[ "$type" -eq "1" ]]; then
+                echo "<tr><td>$type</td><td>$count</td></tr>" >> $outputFile
+            else
+                echo "<tr><td><a href='coincidences_${sample}_${type}.txt'>$type</a></td><td>$count</td></tr>" >> $outputFile
+            fi
+        done < $outputDir/ClonalityOverView_$sample.txt
+        echo "</table></div>" >> $outputFile
 	done
 	
 	cat $dir/naive_clonality.htm >> $outputFile
@@ -295,7 +281,7 @@
 	echo "<table class='pure-table pure-table-striped' id='junction_table'> <caption>Unproductive mean</caption><thead><tr><th>Donor</th><th>Number of sequences</th><th>V.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.D</th><th>D.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.J</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><th>Median.CDR3</th><thead></tr><tbody>" >> $outputFile
 	while read Sample unique VDEL P1 N1 P2 DELD DDEL P3 N2 P4 DELJ TotalDel TotalN TotalP median
 	do
-		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELD</td><td>$DDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>$median</td></tr>" >> $outputFile
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELD</td><td>$DDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>-</td></tr>" >> $outputFile
 	done < $outputDir/junctionAnalysisUnProd_mean_wD.txt
 	echo "</tbody></table>" >> $outputFile
 	
@@ -309,7 +295,7 @@
 	echo "<table class='pure-table pure-table-striped' id='junction_table'> <caption>Unproductive median</caption><thead><tr><th>Donor</th><th>Number of sequences</th><th>V.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.D</th><th>D.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.J</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><th>Median.CDR3</th><thead></tr><tbody>" >> $outputFile
 	while read Sample unique VDEL P1 N1 P2 DELD DDEL P3 N2 P4 DELJ TotalDel TotalN TotalP median
 	do
-		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELD</td><td>$DDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>$median</td></tr>" >> $outputFile
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELD</td><td>$DDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>-</td></tr>" >> $outputFile
 	done < $outputDir/junctionAnalysisUnProd_median_wD.txt
 	echo "</tbody></table>" >> $outputFile
 	
@@ -325,7 +311,7 @@
 	echo "<table class='pure-table pure-table-striped' id='junction_table'> <caption>Unproductive mean</caption><thead><tr><th>Donor</th><th>Number of sequences</th><th>V.DEL</th><th>P1</th><th>N</th><th>P2</th><th>DEL.J</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><th>Median.CDR3</th><thead></tr><tbody>" >> $outputFile
 	while read Sample unique VDEL P1 N1 P2 DELJ TotalDel TotalN TotalP median
 	do
-		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>$median</td></tr>" >> $outputFile
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>-</td></tr>" >> $outputFile
 	done < $outputDir/junctionAnalysisUnProd_mean_nD.txt
 	echo "</tbody></table>" >> $outputFile
 	
@@ -339,7 +325,7 @@
 	echo "<table class='pure-table pure-table-striped' id='junction_table'> <caption>Unproductive median</caption><thead><tr><th>Donor</th><th>Number of sequences</th><th>V.DEL</th><th>P1</th><th>N</th><th>P2</th><th>DEL.J</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><th>Median.CDR3</th><thead></tr><tbody>" >> $outputFile
 	while read Sample unique VDEL P1 N1 P2 DELJ TotalDel TotalN TotalP median
 	do
-		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>$median</td></tr>" >> $outputFile
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELJ</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td><td>-/td></tr>" >> $outputFile
 	done < $outputDir/junctionAnalysisUnProd_median_nD.txt
 	echo "</tbody></table>" >> $outputFile
 	
diff -r 94765af0db1f -r 28fbbdfd7a87 report_clonality/r_wrapper.sh~
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/report_clonality/r_wrapper.sh~	Mon Feb 13 09:08:46 2017 -0500
@@ -0,0 +1,203 @@
+#!/bin/bash
+
+inputFile=$1
+outputDir=$3
+outputFile=$3/index.html #$2
+clonalType=$4
+species=$5
+locus=$6
+filterproductive=$7
+clonality_method=$8
+dir="$(cd "$(dirname "$0")" && pwd)"
+useD="false"
+if grep -q "$species.*${locus}D" "$dir/genes.txt" ; then
+	echo "species D region in reference db"
+	useD="true"
+fi
+echo "$species"
+if [[ "$species" == *"custom"* ]] ; then
+	loci=(${locus//;/ })
+	useD="true"
+	echo "${loci[@]}"
+	if [[ "${#loci[@]}" -eq "2" ]] ; then
+		useD="false"
+	fi
+fi
+mkdir $3
+cp $dir/genes.txt $outputDir
+Rscript --verbose $dir/RScript.r $inputFile $outputDir $outputDir $clonalType "$species" "$locus" $filterproductive ${clonality_method} 2>&1
+cp $dir/tabber.js $outputDir
+cp $dir/style.css $outputDir
+cp $dir/script.js $outputDir
+cp $dir/jquery-1.11.0.min.js $outputDir
+samples=`cat $outputDir/samples.txt`
+echo "<html><center><h1><a href='index.html'>Click here for the results</a></h1>Tip: Open it in a new tab (middle mouse button or right mouse button -> 'open in new tab' on the link above)<br />" > $2
+echo "<table border = 1>" >> $2
+echo "<thead><tr><th>Sample/Replicate</th><th>All</th><th>Productive</th><th>Unique Productive</th><th>Unproductive</th><th>Unique Unproductive</th></tr></thead>" >> $2
+while IFS=, read sample all productive perc_prod productive_unique perc_prod_un unproductive perc_unprod unproductive_unique perc_unprod_un
+	do
+		echo "<tr><td>$sample</td>" >> $2
+		echo "<td>$all</td>" >> $2
+		echo "<td>$productive (${perc_prod}%)</td>" >> $2
+		echo "<td>$productive_unique (${perc_prod_un}%)</td>" >> $2
+		echo "<td>$unproductive (${perc_unprod}%)</td>" >> $2
+		echo "<td>$unproductive_unique (${perc_unprod_un}%)</td></tr>" >> $2
+done < $outputDir/productive_counting.txt
+echo "</table border></center></html>" >> $2
+
+echo "productive_counting.txt"
+echo "<html><head><title>Report on:" >> $outputFile
+for sample in $samples; do
+	echo " $sample" >> $outputFile
+done
+echo "</title><script type='text/javascript' src='jquery-1.11.0.min.js'></script>" >> $outputFile
+echo "<script type='text/javascript' src='tabber.js'></script>" >> $outputFile
+echo "<script type='text/javascript' src='script.js'></script>" >> $outputFile
+echo "<link rel='stylesheet' type='text/css' href='style.css'></head>" >> $outputFile
+echo "<div class='tabber'><div class='tabbertab' title='Gene frequencies'>" >> $outputFile
+
+echo "<img src='CDR3LengthPlot.png'/><br />" >> $outputFile
+echo "<img src='VFPlot.png'/>" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+	echo "<img src='DFPlot.png'/>" >> $outputFile
+fi
+echo "<img src='JFPlot.png'/>" >> $outputFile
+echo "<img src='VPlot.png'/>" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+	echo "<img src='DPlot.png'/>" >> $outputFile
+fi
+echo "<img src='JPlot.png'/>" >> $outputFile
+echo "<img src='AAComposition.png'/></div>" >> $outputFile
+
+count=1
+echo "<div class='tabbertab' title='Heatmaps'><div class='tabber'>" >> $outputFile
+for sample in $samples; do
+	echo "<div class='tabbertab' title='$sample'><table border='1'><tr>" >> $outputFile
+	if [[ "$useD" == "true" ]] ; then
+		echo "<td><img src='HeatmapVD_$sample.png'/></td>" >> $outputFile
+	fi
+	echo "<td><img src='HeatmapVJ_$sample.png'/></td>" >> $outputFile
+	if [[ "$useD" == "true" ]] ; then
+		echo "<td><img src='HeatmapDJ_$sample.png'/></td>" >> $outputFile
+	fi
+	echo "</tr></table></div>" >> $outputFile
+	count=$((count+1))
+done
+echo "</div></div>" >> $outputFile
+
+#echo "<div class='tabbertab' title='Interactive'><svg class='chart'></svg><script src='http://d3js.org/d3.v3.min.js'></script></div>" >> $outputFile
+
+hasReplicateColumn="$(if head -n 1 $inputFile | grep -q 'Replicate'; then echo 'Yes'; else echo 'No'; fi)"
+echo "$hasReplicateColumn"
+#if its a 'new' merged file with replicate info
+if [[ "$hasReplicateColumn" == "Yes" && "$clonalType" != "none" ]] ; then
+	echo "<div class='tabbertab' title='Clonality'><div class='tabber'>" >> $outputFile
+	for sample in $samples; do
+		echo "${clonality_method}"
+		if [[ "${clonality_method}" == "old" ]] ; then
+			echo "in old"
+			clonalityScore="$(cat $outputDir/ClonalityScore_$sample.csv)"
+			echo "<div class='tabbertab' title='$sample'><table border='1'>" >> $outputFile
+			echo "<tr><td colspan='4'>Clonality Score: $clonalityScore</td></tr>" >> $outputFile
+
+			#replicate,reads,squared
+			echo "<tr><td>Replicate ID</td><td>Number of Reads</td><td>Reads Squared</td><td></td></tr>" >> $outputFile
+			while IFS=, read replicate reads squared
+			do
+				
+				echo "<tr><td>$replicate</td><td>$reads</td><td>$squared</td><td></td></tr>" >> $outputFile
+			done < $outputDir/ReplicateReads_$sample.csv
+			
+			#sum of reads and reads squared
+			while IFS=, read readsSum squaredSum
+				do
+					echo "<tr><td>Sum</td><td>$readsSum</td><td>$squaredSum</td></tr>" >> $outputFile
+			done < $outputDir/ReplicateSumReads_$sample.csv
+			
+			#overview
+			echo "<tr><td>Coincidence Type</td><td>Raw Coincidence Freq</td><td>Coincidence Weight</td><td>Coincidences, Weighted</td></tr>" >> $outputFile
+			while IFS=, read type count weight weightedCount
+			do
+				echo "<tr><td>$type</td><td>$count</td><td>$weight</td><td>$weightedCount</td></tr>" >> $outputFile
+			done < $outputDir/ClonalityOverView_$sample.csv
+			echo "</table></div>" >> $outputFile
+		else
+			echo "in new"
+			clonalityScore="$(cat $outputDir/lymphclon_clonality_${sample}.csv)"
+			echo "<div class='tabbertab' title='$sample'>" >> $outputFile
+			echo "Lymphclon clonality score: <br />$clonalityScore<br /><br />" >> $outputFile
+			echo "<table border = 1>" >> $outputFile
+			while IFS=, read type count
+			do
+				echo "<tr><td>$type</td><td>$count</td></tr>" >> $outputFile
+			done < $outputDir/lymphclon_coincidences_$sample.csv
+			echo "</table></div>" >> $outputFile
+		fi
+	done
+	echo "</div></div>" >> $outputFile
+fi
+
+hasJunctionData="$(if head -n 1 $inputFile | grep -q '3V-REGION trimmed-nt'; then echo 'Yes'; else echo 'No'; fi)"
+
+if [[ "$hasJunctionData" == "Yes" ]] ; then
+	echo "<div class='tabbertab' title='Junction Analysis'>" >> $outputFile
+	echo "<table border='1' id='junction_table'> <caption>Productive</caption><thead><tr><th>Sample</th><th>count</th><th>VH.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.DH</th><th>DH.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.JH</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><thead></tr><tbody>" >> $outputFile
+	while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP
+	do
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VHDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELDH</td><td>$DHDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJH</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td></tr>" >> $outputFile
+	done < $outputDir/junctionAnalysisProd.csv
+	echo "</tbody></table>" >> $outputFile
+	
+	echo "<table border='1' id='junction_table'> <caption>Unproductive</caption><thead><tr><th>Sample</th><th>count</th><th>VH.DEL</th><th>P1</th><th>N1</th><th>P2</th><th>DEL.DH</th><th>DH.DEL</th><th>P3</th><th>N2</th><th>P4</th><th>DEL.JH</th><th>Total.Del</th><th>Total.N</th><th>Total.P</th><thead></tr><tbody>" >> $outputFile
+	while IFS=, read Sample unique VHDEL P1 N1 P2 DELDH DHDEL P3 N2 P4 DELJH TotalDel TotalN TotalP
+	do
+		echo "<tr><td>$Sample</td><td>$unique</td><td>$VHDEL</td><td>$P1</td><td>$N1</td><td>$P2</td><td>$DELDH</td><td>$DHDEL</td><td>$P3</td><td>$N2</td><td>$P4</td><td>$DELJH</td><td>$TotalDel</td><td>$TotalN</td><td>$TotalP</td></tr>" >> $outputFile
+	done < $outputDir/junctionAnalysisUnProd.csv
+	echo "</tbody></table>" >> $outputFile
+	
+	echo "</div>" >> $outputFile
+fi
+
+echo "<div class='tabbertab' title='Comparison'><table border='1'><tr><th>ID</th><th>Include</th></tr>" >> $outputFile
+for sample in $samples; do
+	echo "<tr><td>$sample</td><td><input type='checkbox' onchange=\"javascript:compareAdd('$sample')\" id='compare_checkbox_$sample'/></td></tr>" >> $outputFile
+done
+echo "</table><div name='comparisonarea'>" >> $outputFile
+echo "<table><tr id='comparison_table_vd'></tr></table>" >> $outputFile
+echo "<table><tr id='comparison_table_vj'></tr></table>" >> $outputFile
+echo "<table><tr id='comparison_table_dj'></tr></table>" >> $outputFile
+echo "</div></div>" >> $outputFile
+
+echo "<div class='tabbertab' title='Downloads'>" >> $outputFile
+echo "<table border='1'>" >> $outputFile
+echo "<tr><th>Description</th><th>Link</th></tr>" >> $outputFile
+echo "<tr><td>The dataset used to generate the frequency graphs and the heatmaps (Unique based on clonaltype, $clonalType)</td><td><a href='allUnique.csv'>Download</a></td></tr>" >> $outputFile
+echo "<tr><td>The dataset used to calculate clonality score (Unique based on clonaltype, $clonalType)</td><td><a href='clonalityComplete.csv'>Download</a></td></tr>" >> $outputFile
+
+echo "<tr><td>The dataset used to generate the CDR3 length frequency graph</td><td><a href='CDR3LengthPlot.csv'>Download</a></td></tr>" >> $outputFile
+
+echo "<tr><td>The dataset used to generate the V gene family frequency graph</td><td><a href='VFFrequency.csv'>Download</a></td></tr>" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+	echo "<tr><td>The dataset used to generate the D gene family frequency graph</td><td><a href='DFFrequency.csv'>Download</a></td></tr>" >> $outputFile
+fi
+echo "<tr><td>The dataset used to generate the J gene family frequency graph</td><td><a href='JFFrequency.csv'>Download</a></td></tr>" >> $outputFile
+
+echo "<tr><td>The dataset used to generate the V gene frequency graph</td><td><a href='VFrequency.csv'>Download</a></td></tr>" >> $outputFile
+if [[ "$useD" == "true" ]] ; then
+	echo "<tr><td>The dataset used to generate the D gene frequency graph</td><td><a href='DFrequency.csv'>Download</a></td></tr>" >> $outputFile
+fi
+echo "<tr><td>The dataset used to generate the J gene frequency graph</td><td><a href='JFrequency.csv'>Download</a></td></tr>" >> $outputFile
+echo "<tr><td>The dataset used to generate the AA composition graph</td><td><a href='AAComposition.csv'>Download</a></td></tr>" >> $outputFile
+
+for sample in $samples; do
+	if [[ "$useD" == "true" ]] ; then
+		echo "<tr><td>The data used to generate the VD heatmap for $sample.</td><td><a href='HeatmapVD_$sample.csv'>Download</a></td></tr>" >> $outputFile
+	fi
+	echo "<tr><td>The data used to generate the VJ heatmap for $sample.</td><td><a href='HeatmapVJ_$sample.csv'>Download</a></td></tr>" >> $outputFile
+	if [[ "$useD" == "true" ]] ; then
+		echo "<tr><td>The data used to generate the DJ heatmap for $sample.</td><td><a href='HeatmapDJ_$sample.csv'>Download</a></td></tr>" >> $outputFile
+	fi
+done
+
+echo "</table>" >> $outputFile
+echo "</div></html>" >> $outputFile