Mercurial > repos > davidvanzessen > shm_csr
view merge_and_filter.r @ 56:ee807645b224 draft
Uploaded
author | davidvanzessen |
---|---|
date | Mon, 17 Jul 2017 10:44:40 -0400 |
parents | 6cd12c71c3d3 |
children | cb779a45537b |
line wrap: on
line source
args <- commandArgs(trailingOnly = TRUE) summaryfile = args[1] sequencesfile = args[2] mutationanalysisfile = args[3] mutationstatsfile = args[4] hotspotsfile = args[5] aafile = args[6] gene_identification_file= args[7] output = args[8] before.unique.file = args[9] unmatchedfile = args[10] method=args[11] functionality=args[12] unique.type=args[13] filter.unique=args[14] filter.unique.count=as.numeric(args[15]) class.filter=args[16] empty.region.filter=args[17] print(paste("filter.unique.count:", filter.unique.count)) summ = read.table(summaryfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") sequences = read.table(sequencesfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") mutationanalysis = read.table(mutationanalysisfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") mutationstats = read.table(mutationstatsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") hotspots = read.table(hotspotsfile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") AAs = read.table(aafile, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") gene_identification = read.table(gene_identification_file, header=T, sep="\t", fill=T, stringsAsFactors=F, quote="") fix_column_names = function(df){ if("V.DOMAIN.Functionality" %in% names(df)){ names(df)[names(df) == "V.DOMAIN.Functionality"] = "Functionality" print("found V.DOMAIN.Functionality, changed") } if("V.DOMAIN.Functionality.comment" %in% names(df)){ names(df)[names(df) == "V.DOMAIN.Functionality.comment"] = "Functionality.comment" print("found V.DOMAIN.Functionality.comment, changed") } return(df) } fix_non_unique_ids = function(df){ df$Sequence.ID = paste(df$Sequence.ID, 1:nrow(df)) return(df) } summ = fix_column_names(summ) sequences = fix_column_names(sequences) mutationanalysis = fix_column_names(mutationanalysis) mutationstats = fix_column_names(mutationstats) hotspots = fix_column_names(hotspots) AAs = fix_column_names(AAs) if(method == "blastn"){ #"qseqid\tsseqid\tpident\tlength\tmismatch\tgapopen\tqstart\tqend\tsstart\tsend\tevalue\tbitscore" gene_identification = gene_identification[!duplicated(gene_identification$qseqid),] ref_length = data.frame(sseqid=c("ca1", "ca2", "cg1", "cg2", "cg3", "cg4", "cm"), ref.length=c(81,81,141,141,141,141,52)) gene_identification = merge(gene_identification, ref_length, by="sseqid", all.x=T) gene_identification$chunk_hit_percentage = (gene_identification$length / gene_identification$ref.length) * 100 gene_identification = gene_identification[,c("qseqid", "chunk_hit_percentage", "pident", "qstart", "sseqid")] colnames(gene_identification) = c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match") } #print("Summary analysis files columns") #print(names(summ)) input.sequence.count = nrow(summ) print(paste("Number of sequences in summary file:", input.sequence.count)) filtering.steps = data.frame(character(0), numeric(0)) filtering.steps = rbind(filtering.steps, c("Input", input.sequence.count)) filtering.steps[,1] = as.character(filtering.steps[,1]) filtering.steps[,2] = as.character(filtering.steps[,2]) #filtering.steps[,3] = as.numeric(filtering.steps[,3]) #print("summary files columns") #print(names(summ)) summ = merge(summ, gene_identification, by="Sequence.ID") print(paste("Number of sequences after merging with gene identification:", nrow(summ))) summ = summ[summ$Functionality != "No results",] print(paste("Number of sequences after 'No results' filter:", nrow(summ))) filtering.steps = rbind(filtering.steps, c("After 'No results' filter", nrow(summ))) if(functionality == "productive"){ summ = summ[summ$Functionality == "productive (see comment)" | summ$Functionality == "productive",] } else if (functionality == "unproductive"){ summ = summ[summ$Functionality == "unproductive (see comment)" | summ$Functionality == "unproductive",] } else if (functionality == "remove_unknown"){ summ = summ[summ$Functionality != "No results" & summ$Functionality != "unknown (see comment)" & summ$Functionality != "unknown",] } print(paste("Number of sequences after functionality filter:", nrow(summ))) filtering.steps = rbind(filtering.steps, c("After functionality filter", nrow(summ))) if(F){ #to speed up debugging set.seed(1) summ = summ[sample(nrow(summ), floor(nrow(summ) * 0.03)),] print(paste("Number of sequences after sampling 3%:", nrow(summ))) filtering.steps = rbind(filtering.steps, c("Number of sequences after sampling 3%", nrow(summ))) } print("mutation analysis files columns") print(names(mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])])) print(head(summ$Sequence.ID)) print("_-------------------------------------") print(head(mutationanalysis$Sequence.ID)) result = merge(summ, mutationanalysis[,!(names(mutationanalysis) %in% names(summ)[-1])], by="Sequence.ID") print(paste("Number of sequences after merging with mutation analysis file:", nrow(result))) #print("mutation stats files columns") #print(names(mutationstats[,!(names(mutationstats) %in% names(result)[-1])])) result = merge(result, mutationstats[,!(names(mutationstats) %in% names(result)[-1])], by="Sequence.ID") print(paste("Number of sequences after merging with mutation stats file:", nrow(result))) print("hotspots files columns") print(names(hotspots[,!(names(hotspots) %in% names(result)[-1])])) result = merge(result, hotspots[,!(names(hotspots) %in% names(result)[-1])], by="Sequence.ID") print(paste("Number of sequences after merging with hotspots file:", nrow(result))) print("sequences files columns") print(c("FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")) sequences = sequences[,c("Sequence.ID", "FR1.IMGT", "CDR1.IMGT", "FR2.IMGT", "CDR2.IMGT", "FR3.IMGT", "CDR3.IMGT")] names(sequences) = c("Sequence.ID", "FR1.IMGT.seq", "CDR1.IMGT.seq", "FR2.IMGT.seq", "CDR2.IMGT.seq", "FR3.IMGT.seq", "CDR3.IMGT.seq") result = merge(result, sequences, by="Sequence.ID", all.x=T) print("sequences files columns") print("CDR3.IMGT") AAs = AAs[,c("Sequence.ID", "CDR3.IMGT")] names(AAs) = c("Sequence.ID", "CDR3.IMGT.AA") result = merge(result, AAs, by="Sequence.ID", all.x=T) print(paste("Number of sequences in result after merging with sequences:", nrow(result))) result$VGene = gsub("^Homsap ", "", result$V.GENE.and.allele) result$VGene = gsub("[*].*", "", result$VGene) result$DGene = gsub("^Homsap ", "", result$D.GENE.and.allele) result$DGene = gsub("[*].*", "", result$DGene) result$JGene = gsub("^Homsap ", "", result$J.GENE.and.allele) result$JGene = gsub("[*].*", "", result$JGene) splt = strsplit(class.filter, "_")[[1]] chunk_hit_threshold = as.numeric(splt[1]) nt_hit_threshold = as.numeric(splt[2]) higher_than=(result$chunk_hit_percentage >= chunk_hit_threshold & result$nt_hit_percentage >= nt_hit_threshold) if(!all(higher_than, na.rm=T)){ #check for no unmatched result[!higher_than,"best_match"] = paste("unmatched,", result[!higher_than,"best_match"]) } if(class.filter == "101_101"){ result$best_match = "all" } write.table(x=result, file=gsub("merged.txt$", "before_filters.txt", output), sep="\t",quote=F,row.names=F,col.names=T) print(paste("Number of empty CDR1 sequences:", sum(result$CDR1.IMGT.seq == "", na.rm=T))) print(paste("Number of empty FR2 sequences:", sum(result$FR2.IMGT.seq == "", na.rm=T))) print(paste("Number of empty CDR2 sequences:", sum(result$CDR2.IMGT.seq == "", na.rm=T))) print(paste("Number of empty FR3 sequences:", sum(result$FR3.IMGT.seq == "", na.rm=T))) if(empty.region.filter == "leader"){ result = result[result$FR1.IMGT.seq != "" & result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] } else if(empty.region.filter == "FR1"){ result = result[result$CDR1.IMGT.seq != "" & result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] } else if(empty.region.filter == "CDR1"){ result = result[result$FR2.IMGT.seq != "" & result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] } else if(empty.region.filter == "FR2"){ result = result[result$CDR2.IMGT.seq != "" & result$FR3.IMGT.seq != "", ] } print(paste("After removal sequences that are missing a gene region:", nrow(result))) filtering.steps = rbind(filtering.steps, c("After removal sequences that are missing a gene region", nrow(result))) if(empty.region.filter == "leader"){ result = result[!(grepl("n|N", result$FR1.IMGT.seq) | grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] } else if(empty.region.filter == "FR1"){ result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR1.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] } else if(empty.region.filter == "CDR1"){ result = result[!(grepl("n|N", result$FR2.IMGT.seq) | grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] } else if(empty.region.filter == "FR2"){ result = result[!(grepl("n|N", result$FR3.IMGT.seq) | grepl("n|N", result$CDR2.IMGT.seq) | grepl("n|N", result$CDR3.IMGT.seq)),] } print(paste("Number of sequences in result after n filtering:", nrow(result))) filtering.steps = rbind(filtering.steps, c("After N filter", nrow(result))) cleanup_columns = c("FR1.IMGT.Nb.of.mutations", "CDR1.IMGT.Nb.of.mutations", "FR2.IMGT.Nb.of.mutations", "CDR2.IMGT.Nb.of.mutations", "FR3.IMGT.Nb.of.mutations") for(col in cleanup_columns){ result[,col] = gsub("\\(.*\\)", "", result[,col]) result[,col] = as.numeric(result[,col]) result[is.na(result[,col]),] = 0 } write.table(result, before.unique.file, sep="\t", quote=F,row.names=F,col.names=T) if(filter.unique != "no"){ clmns = names(result) if(empty.region.filter == "leader"){ result$unique.def = paste(result$FR1.IMGT.seq, result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) } else if(empty.region.filter == "FR1"){ result$unique.def = paste(result$CDR1.IMGT.seq, result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) } else if(empty.region.filter == "CDR1"){ result$unique.def = paste(result$FR2.IMGT.seq, result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) } else if(empty.region.filter == "FR2"){ result$unique.def = paste(result$CDR2.IMGT.seq, result$FR3.IMGT.seq, result$CDR3.IMGT.seq) } if(filter.unique == "remove"){ result = result[duplicated(result$unique.def) | duplicated(result$unique.def, fromLast=T),] unique.defs = data.frame(table(result$unique.def)) unique.defs = unique.defs[unique.defs$Freq >= filter.unique.count,] result = result[result$unique.def %in% unique.defs$Var1,] } result$unique.def = paste(result$unique.def, gsub(",.*", "", result$best_match)) #keep the unique sequences that are in multiple classes, gsub so the unmatched don't have a class after it result = result[!duplicated(result$unique.def),] } write.table(result, gsub("before_unique_filter.txt", "after_unique_filter.txt", before.unique.file), sep="\t", quote=F,row.names=F,col.names=T) filtering.steps = rbind(filtering.steps, c("After filter unique sequences", nrow(result))) print(paste("Number of sequences in result after unique filtering:", nrow(result))) if(nrow(summ) == 0){ stop("No data remaining after filter") } result$best_match_class = gsub(",.*", "", result$best_match) #gsub so the unmatched don't have a class after it #result$past = "" #cls = unlist(strsplit(unique.type, ",")) #for (i in 1:nrow(result)){ # result[i,"past"] = paste(result[i,cls], collapse=":") #} result$past = do.call(paste, c(result[unlist(strsplit(unique.type, ","))], sep = ":")) result.matched = result[!grepl("unmatched", result$best_match),] result.unmatched = result[grepl("unmatched", result$best_match),] result = rbind(result.matched, result.unmatched) result = result[!(duplicated(result$past)), ] result = result[,!(names(result) %in% c("past", "best_match_class"))] print(paste("Number of sequences in result after", unique.type, "filtering:", nrow(result))) filtering.steps = rbind(filtering.steps, c("After remove duplicates based on filter", nrow(result))) unmatched = result[grepl("^unmatched", result$best_match),c("Sequence.ID", "chunk_hit_percentage", "nt_hit_percentage", "start_locations", "best_match")] print(paste("Number of rows in result:", nrow(result))) print(paste("Number of rows in unmatched:", nrow(unmatched))) matched.sequences = result[!grepl("^unmatched", result$best_match),] write.table(x=matched.sequences, file=gsub("merged.txt$", "filtered.txt", output), sep="\t",quote=F,row.names=F,col.names=T) matched.sequences.count = nrow(matched.sequences) unmatched.sequences.count = sum(grepl("^unmatched", result$best_match)) filtering.steps = rbind(filtering.steps, c("Number of matched sequences", matched.sequences.count)) filtering.steps = rbind(filtering.steps, c("Number of unmatched sequences", unmatched.sequences.count)) filtering.steps[,2] = as.numeric(filtering.steps[,2]) filtering.steps$perc = round(filtering.steps[,2] / input.sequence.count * 100, 2) write.table(x=filtering.steps, file=gsub("unmatched", "filtering_steps", unmatchedfile), sep="\t",quote=F,row.names=F,col.names=F) write.table(x=result, file=output, sep="\t",quote=F,row.names=F,col.names=T) write.table(x=unmatched, file=unmatchedfile, sep="\t",quote=F,row.names=F,col.names=T)