Mercurial > repos > davidvanzessen > shm_csr
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| author | davidvanzessen |
|---|---|
| date | Tue, 23 May 2017 08:32:58 -0400 |
| parents | 77a7ac76c7b9 |
| children | aa8d37bd1930 |
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library(data.table) library(ggplot2) library(reshape2) args <- commandArgs(trailingOnly = TRUE) input = args[1] genes = unlist(strsplit(args[2], ",")) outputdir = args[3] empty.region.filter = args[4] setwd(outputdir) dat = read.table(input, header=T, sep="\t", fill=T, stringsAsFactors=F) if(length(dat$Sequence.ID) == 0){ setwd(outputdir) result = data.frame(x = rep(0, 5), y = rep(0, 5), z = rep(NA, 5)) row.names(result) = c("Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)") write.table(x=result, file="mutations.txt", sep=",",quote=F,row.names=T,col.names=F) transitionTable = data.frame(A=rep(0, 4),C=rep(0, 4),G=rep(0, 4),T=rep(0, 4)) row.names(transitionTable) = c("A", "C", "G", "T") transitionTable["A","A"] = NA transitionTable["C","C"] = NA transitionTable["G","G"] = NA transitionTable["T","T"] = NA write.table(x=transitionTable, file="transitions.txt", sep=",",quote=F,row.names=T,col.names=NA) cat("0", file="n.txt") stop("No data") } cleanup_columns = c("FR1.IMGT.c.a", "FR2.IMGT.g.t", "CDR1.IMGT.Nb.of.nucleotides", "CDR2.IMGT.t.a", "FR1.IMGT.c.g", "CDR1.IMGT.c.t", "FR2.IMGT.a.c", "FR2.IMGT.Nb.of.mutations", "FR2.IMGT.g.c", "FR2.IMGT.a.g", "FR3.IMGT.t.a", "FR3.IMGT.t.c", "FR2.IMGT.g.a", "FR3.IMGT.c.g", "FR1.IMGT.Nb.of.mutations", "CDR1.IMGT.g.a", "CDR1.IMGT.t.g", "CDR1.IMGT.g.c", "CDR2.IMGT.Nb.of.nucleotides", "FR2.IMGT.a.t", "CDR1.IMGT.Nb.of.mutations", "CDR3.IMGT.Nb.of.nucleotides", "CDR1.IMGT.a.g", "FR3.IMGT.a.c", "FR1.IMGT.g.a", "FR3.IMGT.a.g", "FR1.IMGT.a.t", "CDR2.IMGT.a.g", "CDR2.IMGT.Nb.of.mutations", "CDR2.IMGT.g.t", "CDR2.IMGT.a.c", "CDR1.IMGT.t.c", "FR3.IMGT.g.c", "FR1.IMGT.g.t", "FR3.IMGT.g.t", "CDR1.IMGT.a.t", "FR1.IMGT.a.g", "FR3.IMGT.a.t", "FR3.IMGT.Nb.of.nucleotides", "FR2.IMGT.t.c", "CDR2.IMGT.g.a", "FR2.IMGT.t.a", "CDR1.IMGT.t.a", "FR2.IMGT.t.g", "FR3.IMGT.t.g", "FR2.IMGT.Nb.of.nucleotides", "FR1.IMGT.t.a", "FR1.IMGT.t.g", "FR3.IMGT.c.t", "FR1.IMGT.t.c", "CDR2.IMGT.a.t", "FR2.IMGT.c.t", "CDR1.IMGT.g.t", "CDR2.IMGT.t.g", "FR1.IMGT.Nb.of.nucleotides", "CDR1.IMGT.c.g", "CDR2.IMGT.t.c", "FR3.IMGT.g.a", "CDR1.IMGT.a.c", "FR2.IMGT.c.a", "FR3.IMGT.Nb.of.mutations", "FR2.IMGT.c.g", "CDR2.IMGT.g.c", "FR1.IMGT.g.c", "CDR2.IMGT.c.t", "FR3.IMGT.c.a", "CDR1.IMGT.c.a", "CDR2.IMGT.c.g", "CDR2.IMGT.c.a", "FR1.IMGT.c.t", "FR1.IMGT.Nb.of.silent.mutations", "FR2.IMGT.Nb.of.silent.mutations", "FR3.IMGT.Nb.of.silent.mutations", "FR1.IMGT.Nb.of.nonsilent.mutations", "FR2.IMGT.Nb.of.nonsilent.mutations", "FR3.IMGT.Nb.of.nonsilent.mutations") print("Cleaning up columns") for(col in cleanup_columns){ dat[,col] = gsub("\\(.*\\)", "", dat[,col]) #dat[dat[,col] == "",] = "0" dat[,col] = as.numeric(dat[,col]) dat[is.na(dat[,col]),col] = 0 } regions = c("FR1", "CDR1", "FR2", "CDR2", "FR3") if(empty.region.filter == "FR1") { regions = c("CDR1", "FR2", "CDR2", "FR3") } else if (empty.region.filter == "CDR1") { regions = c("FR2", "CDR2", "FR3") } else if (empty.region.filter == "FR2") { regions = c("CDR2", "FR3") } pdfplots = list() #save() this later to create the pdf plots in another script (maybe avoids the "address (nil), cause memory not mapped") sum_by_row = function(x, columns) { sum(as.numeric(x[columns]), na.rm=T) } print("aggregating data into new columns") VRegionMutations_columns = paste(regions, ".IMGT.Nb.of.mutations", sep="") dat$VRegionMutations = apply(dat, FUN=sum_by_row, 1, columns=VRegionMutations_columns) VRegionNucleotides_columns = paste(regions, ".IMGT.Nb.of.nucleotides", sep="") dat$FR3.IMGT.Nb.of.nucleotides = nchar(dat$FR3.IMGT.seq) dat$VRegionNucleotides = apply(dat, FUN=sum_by_row, 1, columns=VRegionNucleotides_columns) transitionMutations_columns = paste(rep(regions, each=4), c(".IMGT.a.g", ".IMGT.g.a", ".IMGT.c.t", ".IMGT.t.c"), sep="") dat$transitionMutations = apply(dat, FUN=sum_by_row, 1, columns=transitionMutations_columns) transversionMutations_columns = paste(rep(regions, each=8), c(".IMGT.a.c",".IMGT.c.a",".IMGT.a.t",".IMGT.t.a",".IMGT.g.c",".IMGT.c.g",".IMGT.g.t",".IMGT.t.g"), sep="") dat$transversionMutations = apply(dat, FUN=sum_by_row, 1, columns=transversionMutations_columns) transitionMutationsAtGC_columns = paste(rep(regions, each=2), c(".IMGT.g.a",".IMGT.c.t"), sep="") dat$transitionMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtGC_columns) totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.c.g",".IMGT.c.t",".IMGT.c.a",".IMGT.g.c",".IMGT.g.a",".IMGT.g.t"), sep="") #totalMutationsAtGC_columns = paste(rep(regions, each=6), c(".IMGT.g.a",".IMGT.c.t",".IMGT.c.a",".IMGT.c.g",".IMGT.g.t"), sep="") dat$totalMutationsAtGC = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtGC_columns) transitionMutationsAtAT_columns = paste(rep(regions, each=2), c(".IMGT.a.g",".IMGT.t.c"), sep="") dat$transitionMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=transitionMutationsAtAT_columns) totalMutationsAtAT_columns = paste(rep(regions, each=6), c(".IMGT.a.g",".IMGT.a.c",".IMGT.a.t",".IMGT.t.g",".IMGT.t.c",".IMGT.t.a"), sep="") #totalMutationsAtAT_columns = paste(rep(regions, each=5), c(".IMGT.a.g",".IMGT.t.c",".IMGT.a.c",".IMGT.g.c",".IMGT.t.g"), sep="") dat$totalMutationsAtAT = apply(dat, FUN=sum_by_row, 1, columns=totalMutationsAtAT_columns) FRRegions = regions[grepl("FR", regions)] CDRRegions = regions[grepl("CDR", regions)] FR_silentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.silent.mutations", sep="") dat$silentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_silentMutations_columns) CDR_silentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.silent.mutations", sep="") dat$silentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_silentMutations_columns) FR_nonSilentMutations_columns = paste(FRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") dat$nonSilentMutationsFR = apply(dat, FUN=sum_by_row, 1, columns=FR_nonSilentMutations_columns) CDR_nonSilentMutations_columns = paste(CDRRegions, ".IMGT.Nb.of.nonsilent.mutations", sep="") dat$nonSilentMutationsCDR = apply(dat, FUN=sum_by_row, 1, columns=CDR_nonSilentMutations_columns) mutation.sum.columns = c("Sequence.ID", "VRegionMutations", "VRegionNucleotides", "transitionMutations", "transversionMutations", "transitionMutationsAtGC", "transitionMutationsAtAT", "silentMutationsFR", "nonSilentMutationsFR", "silentMutationsCDR", "nonSilentMutationsCDR") write.table(dat[,mutation.sum.columns], "mutation_by_id.txt", sep="\t",quote=F,row.names=F,col.names=T) setwd(outputdir) write.table(dat, input, sep="\t",quote=F,row.names=F,col.names=T) base.order.x = data.frame(base=c("A", "C", "G", "T"), order.x=1:4) base.order.y = data.frame(base=c("T", "G", "C", "A"), order.y=1:4) calculate_result = function(i, gene, dat, matrx, f, fname, name){ tmp = dat[grepl(paste("^", gene, ".*", sep=""), dat$best_match),] j = i - 1 x = (j * 3) + 1 y = (j * 3) + 2 z = (j * 3) + 3 if(nrow(tmp) > 0){ if(fname == "sum"){ matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) matrx[1,z] = round(f(matrx[1,x] / matrx[1,y]) * 100, digits=1) } else { matrx[1,x] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[1,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) matrx[1,z] = round(f(tmp$VRegionMutations / tmp$VRegionNucleotides) * 100, digits=1) } matrx[2,x] = round(f(tmp$transitionMutations, na.rm=T), digits=1) matrx[2,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[2,z] = round(matrx[2,x] / matrx[2,y] * 100, digits=1) matrx[3,x] = round(f(tmp$transversionMutations, na.rm=T), digits=1) matrx[3,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[3,z] = round(matrx[3,x] / matrx[3,y] * 100, digits=1) matrx[4,x] = round(f(tmp$transitionMutationsAtGC, na.rm=T), digits=1) matrx[4,y] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) matrx[4,z] = round(matrx[4,x] / matrx[4,y] * 100, digits=1) matrx[5,x] = round(f(tmp$totalMutationsAtGC, na.rm=T), digits=1) matrx[5,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[5,z] = round(matrx[5,x] / matrx[5,y] * 100, digits=1) matrx[6,x] = round(f(tmp$transitionMutationsAtAT, na.rm=T), digits=1) matrx[6,y] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) matrx[6,z] = round(matrx[6,x] / matrx[6,y] * 100, digits=1) matrx[7,x] = round(f(tmp$totalMutationsAtAT, na.rm=T), digits=1) matrx[7,y] = round(f(tmp$VRegionMutations, na.rm=T), digits=1) matrx[7,z] = round(matrx[7,x] / matrx[7,y] * 100, digits=1) matrx[8,x] = round(f(tmp$nonSilentMutationsFR, na.rm=T), digits=1) matrx[8,y] = round(f(tmp$silentMutationsFR, na.rm=T), digits=1) matrx[8,z] = round(matrx[8,x] / matrx[8,y], digits=1) matrx[9,x] = round(f(tmp$nonSilentMutationsCDR, na.rm=T), digits=1) matrx[9,y] = round(f(tmp$silentMutationsCDR, na.rm=T), digits=1) matrx[9,z] = round(matrx[9,x] / matrx[9,y], digits=1) if(fname == "sum"){ regions.fr = regions[grepl("FR", regions)] regions.fr = paste(regions.fr, ".IMGT.Nb.of.nucleotides", sep="") regions.cdr = regions[grepl("CDR", regions)] regions.cdr = paste(regions.cdr, ".IMGT.Nb.of.nucleotides", sep="") if(length(regions.fr) > 1){ #in case there is only on FR region (rowSums needs >1 column) matrx[10,x] = round(f(rowSums(tmp[,regions.fr], na.rm=T)), digits=1) } else { matrx[10,x] = round(f(tmp[,regions.fr], na.rm=T), digits=1) } matrx[10,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) matrx[10,z] = round(matrx[10,x] / matrx[10,y] * 100, digits=1) if(length(regions.cdr) > 1){ #in case there is only on CDR region matrx[11,x] = round(f(rowSums(tmp[,regions.cdr], na.rm=T)), digits=1) } else { matrx[11,x] = round(f(tmp[,regions.cdr], na.rm=T), digits=1) } matrx[11,y] = round(f(tmp$VRegionNucleotides, na.rm=T), digits=1) matrx[11,z] = round(matrx[11,x] / matrx[11,y] * 100, digits=1) } } transitionTable = data.frame(A=zeros,C=zeros,G=zeros,T=zeros) row.names(transitionTable) = c("A", "C", "G", "T") transitionTable["A","A"] = NA transitionTable["C","C"] = NA transitionTable["G","G"] = NA transitionTable["T","T"] = NA if(nrow(tmp) > 0){ for(nt1 in nts){ for(nt2 in nts){ if(nt1 == nt2){ next } NT1 = LETTERS[letters == nt1] NT2 = LETTERS[letters == nt2] FR1 = paste("FR1.IMGT.", nt1, ".", nt2, sep="") CDR1 = paste("CDR1.IMGT.", nt1, ".", nt2, sep="") FR2 = paste("FR2.IMGT.", nt1, ".", nt2, sep="") CDR2 = paste("CDR2.IMGT.", nt1, ".", nt2, sep="") FR3 = paste("FR3.IMGT.", nt1, ".", nt2, sep="") if (empty.region.filter == "leader"){ transitionTable[NT1,NT2] = sum(tmp[,c(FR1, CDR1, FR2, CDR2, FR3)]) } else if (empty.region.filter == "FR1") { transitionTable[NT1,NT2] = sum(tmp[,c(CDR1, FR2, CDR2, FR3)]) } else if (empty.region.filter == "CDR1") { transitionTable[NT1,NT2] = sum(tmp[,c(FR2, CDR2, FR3)]) } else if (empty.region.filter == "FR2") { transitionTable[NT1,NT2] = sum(tmp[,c(CDR2, FR3)]) } } } transition = transitionTable transition$id = names(transition) transition2 = melt(transition, id.vars="id") transition2 = merge(transition2, base.order.x, by.x="id", by.y="base") transition2 = merge(transition2, base.order.y, by.x="variable", by.y="base") transition2[is.na(transition2$value),]$value = 0 if(any(transition2$value != 0)){ #having a transition table filled with 0 is bad print("Plotting heatmap and transition") png(filename=paste("transitions_stacked_", name, ".png", sep="")) p = ggplot(transition2, aes(factor(reorder(id, order.x)), y=value, fill=factor(reorder(variable, order.y)))) + geom_bar(position="fill", stat="identity", colour="black") #stacked bar p = p + xlab("From base") + ylab("") + ggtitle("Bargraph transition information") + guides(fill=guide_legend(title=NULL)) p = p + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) + scale_fill_manual(values=c("A" = "blue4", "G" = "lightblue1", "C" = "olivedrab3", "T" = "olivedrab4")) #p = p + scale_colour_manual(values=c("A" = "black", "G" = "black", "C" = "black", "T" = "black")) print(p) dev.off() pdfplots[[paste("transitions_stacked_", name, ".pdf", sep="")]] <<- p png(filename=paste("transitions_heatmap_", name, ".png", sep="")) p = ggplot(transition2, aes(factor(reorder(variable, -order.y)), factor(reorder(id, -order.x)))) + geom_tile(aes(fill = value)) + scale_fill_gradient(low="white", high="steelblue") #heatmap p = p + xlab("To base") + ylab("From Base") + ggtitle("Heatmap transition information") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) print(p) dev.off() pdfplots[[paste("transitions_heatmap_", name, ".pdf", sep="")]] <<- p } else { #print("No data to plot") } } #print(paste("writing value file: ", name, "_", fname, "_value.txt" ,sep="")) write.table(x=transitionTable, file=paste("transitions_", name ,"_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=NA) write.table(x=tmp[,c("Sequence.ID", "best_match", "chunk_hit_percentage", "nt_hit_percentage", "start_locations")], file=paste("matched_", name , "_", fname, ".txt", sep=""), sep="\t",quote=F,row.names=F,col.names=T) cat(matrx[1,x], file=paste(name, "_", fname, "_value.txt" ,sep="")) cat(nrow(tmp), file=paste(name, "_", fname, "_n.txt" ,sep="")) #print(paste(fname, name, nrow(tmp))) matrx } nts = c("a", "c", "g", "t") zeros=rep(0, 4) funcs = c(median, sum, mean) fnames = c("median", "sum", "mean") print("Creating result tables") for(i in 1:length(funcs)){ func = funcs[[i]] fname = fnames[[i]] print(paste("Creating table for", fname)) rows = 9 if(fname == "sum"){ rows = 11 } matrx = matrix(data = 0, ncol=((length(genes) + 1) * 3),nrow=rows) for(i in 1:length(genes)){ matrx = calculate_result(i, genes[i], dat, matrx, func, fname, genes[i]) } matrx = calculate_result(i + 1, ".*", dat[!grepl("unmatched", dat$best_match),], matrx, func, fname, name="all") result = data.frame(matrx) if(fname == "sum"){ row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR") } else { row.names(result) = c("Number of Mutations (%)", "Transitions (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)") } write.table(x=result, file=paste("mutations_", fname, ".txt", sep=""), sep=",",quote=F,row.names=T,col.names=F) } print("Adding median number of mutations to sum table") sum.table = read.table("mutations_sum.txt", sep=",", header=F) median.table = read.table("mutations_median.txt", sep=",", header=F) new.table = sum.table[1,] new.table[2,] = median.table[1,] new.table[3:12,] = sum.table[2:11,] new.table[,1] = as.character(new.table[,1]) new.table[2,1] = "Median of Number of Mutations (%)" #sum.table = sum.table[c("Number of Mutations (%)", "Median of Number of Mutations (%)", "Transition (%)", "Transversions (%)", "Transitions at G C (%)", "Targeting of G C (%)", "Transitions at A T (%)", "Targeting of A T (%)", "FR R/S (ratio)", "CDR R/S (ratio)", "nt in FR", "nt in CDR"),] write.table(x=new.table, file="mutations_sum.txt", sep=",",quote=F,row.names=F,col.names=F) print("Plotting IGA piechart") dat = dat[!grepl("^unmatched", dat$best_match),] #blegh genesForPlot = dat[grepl("IGA", dat$best_match),]$best_match if(length(genesForPlot) > 0){ genesForPlot = data.frame(table(genesForPlot)) colnames(genesForPlot) = c("Gene","Freq") genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGA1" = "lightblue1", "IGA2" = "blue4")) pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGA subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) write.table(genesForPlot, "IGA_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) png(filename="IGA.png") print(pc) dev.off() pdfplots[["IGA.pdf"]] <- pc } print("Plotting IGG piechart") genesForPlot = dat[grepl("IGG", dat$best_match),]$best_match if(length(genesForPlot) > 0){ genesForPlot = data.frame(table(genesForPlot)) colnames(genesForPlot) = c("Gene","Freq") genesForPlot$label = paste(genesForPlot$Gene, "-", genesForPlot$Freq) pc = ggplot(genesForPlot, aes(x = factor(1), y=Freq, fill=Gene)) pc = pc + geom_bar(width = 1, stat = "identity") + scale_fill_manual(labels=genesForPlot$label, values=c("IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred")) pc = pc + coord_polar(theta="y") + scale_y_continuous(breaks=NULL) pc = pc + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black"), axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank()) pc = pc + xlab(" ") + ylab(" ") + ggtitle(paste("IGG subclass distribution", "( n =", sum(genesForPlot$Freq), ")")) write.table(genesForPlot, "IGG_pie.txt", sep="\t",quote=F,row.names=F,col.names=T) png(filename="IGG.png") print(pc) dev.off() pdfplots[["IGG.pdf"]] <- pc } print("Plotting scatterplot") dat$percentage_mutations = round(dat$VRegionMutations / dat$VRegionNucleotides * 100, 2) dat.clss = dat dat.clss$best_match = substr(dat.clss$best_match, 0, 3) dat.clss = rbind(dat, dat.clss) p = ggplot(dat.clss, aes(best_match, percentage_mutations)) p = p + geom_point(aes(colour=best_match), position="jitter") + geom_boxplot(aes(middle=mean(percentage_mutations)), alpha=0.1, outlier.shape = NA) p = p + xlab("Subclass") + ylab("Frequency") + ggtitle("Frequency scatter plot") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) p = p + scale_fill_manual(values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) p = p + scale_colour_manual(guide = guide_legend(title = "Subclass"), values=c("IGA" = "blue4", "IGA1" = "lightblue1", "IGA2" = "blue4", "IGG" = "olivedrab3", "IGG1" = "olivedrab3", "IGG2" = "red", "IGG3" = "gold", "IGG4" = "darkred", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) png(filename="scatter.png") print(p) dev.off() pdfplots[["scatter.pdf"]] <- p write.table(dat[,c("Sequence.ID", "best_match", "VRegionMutations", "VRegionNucleotides", "percentage_mutations")], "scatter.txt", sep="\t",quote=F,row.names=F,col.names=T) print("Plotting frequency ranges plot") dat$best_match_class = substr(dat$best_match, 0, 3) freq_labels = c("0", "0-2", "2-5", "5-10", "10-15", "15-20", "20") dat$frequency_bins = cut(dat$percentage_mutations, breaks=c(-Inf, 0, 2,5,10,15,20, Inf), labels=freq_labels) frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match_class")]) frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match_class", "frequency_bins")]) frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match_class") frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) p = ggplot(frequency_bins_data, aes(frequency_bins, frequency)) p = p + geom_bar(aes(fill=best_match_class), stat="identity", position="dodge") + theme(panel.background = element_rect(fill = "white", colour="black"), text = element_text(size=16, colour="black")) p = p + xlab("Frequency ranges") + ylab("Frequency") + ggtitle("Mutation Frequencies by class") + scale_fill_manual(guide = guide_legend(title = "Class"), values=c("IGA" = "blue4", "IGG" = "olivedrab3", "IGM" = "darkviolet", "IGE" = "darkorange", "all" = "blue4")) png(filename="frequency_ranges.png") print(p) dev.off() pdfplots[["frequency_ranges.pdf"]] <- p save(pdfplots, file="pdfplots.RData") frequency_bins_data_by_class = frequency_bins_data frequency_bins_data_by_class = frequency_bins_data_by_class[order(frequency_bins_data_by_class$best_match_class, frequency_bins_data_by_class$frequency_bins),] frequency_bins_data_by_class$frequency_bins = gsub("-", " to ", frequency_bins_data_by_class$frequency_bins) frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "20", c("frequency_bins")] = "20 or higher" frequency_bins_data_by_class[frequency_bins_data_by_class$frequency_bins == "0", c("frequency_bins")] = "0 or lower" write.table(frequency_bins_data_by_class, "frequency_ranges_classes.txt", sep="\t",quote=F,row.names=F,col.names=T) frequency_bins_data = data.frame(data.table(dat)[, list(frequency_count=.N), by=c("best_match", "best_match_class", "frequency_bins")]) frequency_bins_sum = data.frame(data.table(dat)[, list(class_sum=sum(.N)), by=c("best_match")]) frequency_bins_data = merge(frequency_bins_data, frequency_bins_sum, by="best_match") frequency_bins_data$frequency = round(frequency_bins_data$frequency_count / frequency_bins_data$class_sum * 100, 2) frequency_bins_data = frequency_bins_data[order(frequency_bins_data$best_match, frequency_bins_data$frequency_bins),] frequency_bins_data$frequency_bins = gsub("-", " to ", frequency_bins_data$frequency_bins) frequency_bins_data[frequency_bins_data$frequency_bins == "20", c("frequency_bins")] = "20 or higher" frequency_bins_data[frequency_bins_data$frequency_bins == "0", c("frequency_bins")] = "0 or lower" write.table(frequency_bins_data, "frequency_ranges_subclasses.txt", sep="\t",quote=F,row.names=F,col.names=T)