Mercurial > repos > bornea > saint_preproc
view pre_process_protein_name_set.R @ 21:9e0a894d2676 draft
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author | bornea |
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date | Thu, 19 Nov 2015 13:38:20 -0500 |
parents | e21be0412789 |
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ins_check_run <- function() { if ("affy" %in% rownames(installed.packages())){} else { source("https://bioconductor.org/biocLite.R") biocLite(c('mygene','affy')) } if ('data.table' %in% rownames(installed.packages())){} else { install.packages('data.table', repos='http://cran.us.r-project.org') } if ('stringr' %in% rownames(installed.packages())){} else { install.packages('stringr', repos='http://cran.us.r-project.org') } if ('VennDiagram' %in% rownames(installed.packages())){} else { install.packages('VennDiagram', repos='http://cran.us.r-project.org') } } ins_check_run() library(data.table) library(affy) library(stringr) library(mygene) library(VennDiagram) ##### #data main <- function(peptides_file, db_path) { peptides_file = read.delim(peptides_file,header=TRUE,stringsAsFactors=FALSE,fill=TRUE) peptides_txt = peptides_file intensity_columns = names(peptides_txt[,str_detect(names(peptides_txt),"Intensity\\.*")]) #Pulls out all lines with Intensity in them. intensity_columns = intensity_columns[2:length(intensity_columns)] #Removes the first column that does not have a bait. peptides_txt_mapped = as.data.frame(map_peptides_proteins(peptides_txt)) #This function as below sets every line to a 1 to 1 intensity to each possible protein. peptides_txt_mapped$Uniprot = str_extract(peptides_txt_mapped$mapped_protein, "[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") #Pulls out just Uniprot id from the script. peptides_txt_mapped = subset(peptides_txt_mapped,!is.na(Uniprot)) #removes reverse sequences and any that didn't match a uniprot accession columns_comb = c("Uniprot", intensity_columns) peptides_mapped_intensity = subset(peptides_txt_mapped, select = columns_comb) #Subsets out only the needed cloumns for Tukeys (Uniprot IDS and baited intensities) swissprot_fasta = scan(db_path, what="character") peptides_txt_mapped_log2 = peptides_mapped_intensity # Takes the log2 of the intensities. for (i in intensity_columns) { peptides_txt_mapped_log2[,i] = log2(subset(peptides_txt_mapped_log2, select = i)) } #get the minimum from each column while ignoring the -Inf; get the min of these mins for the global min; breaks when there's only one intensity column global_min = min(apply(peptides_txt_mapped_log2[,2:ncol(peptides_txt_mapped_log2)],2,function(x) { min(x[x != -Inf]) })) peptides_txt_mapped_log2[peptides_txt_mapped_log2 == -Inf] <- 0 #uniprot accessions WITHOUT isoforms; it looks like only contaminants contain isoforms anyways mapped_protein_uniprotonly = str_extract(peptides_txt_mapped_log2$Uniprot,"[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") mapped_protein_uniprot_accession = str_extract(peptides_txt_mapped_log2$Uniprot,"[OPQ][0-9][A-Z0-9]{3}[0-9](-[0-9]+)?|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}(-[0-9]+)?|[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") peptides_txt_mapped_log2$mapped_protein = mapped_protein_uniprotonly # Runs the Tukey function returning completed table peptides_txt_mapped_log2 = subset(peptides_txt_mapped_log2,mapped_protein %in% swissprot_fasta) protein_intensities_tukeys = get_protein_values(peptides_txt_mapped_log2,intensity_columns) protein_intensities_tukeys[protein_intensities_tukeys == 1] <- 0 write.table(protein_intensities_tukeys, "./tukeys_output.txt", row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t") } map_peptides_proteins = function(peptides_in) { #reverse sequences are blank but have a razor protein indicating that they are reverse; exclude these for now peptides_in = subset(peptides_in,peptides_in$Proteins != "") results_list = list() k = 1 for (i in 1:nrow(peptides_in)) { protein_names = peptides_in[i,"Proteins"] protein_names_split = unlist(strsplit(protein_names,";")) for (j in 1:length(protein_names_split)) { peptides_mapped_proteins = data.frame(peptides_in[i,],mapped_protein=protein_names_split[j],stringsAsFactors=FALSE) results_list[[k]] = peptides_mapped_proteins k = k+1 } } return(rbindlist(results_list)) } get_protein_values = function(mapped_peptides_in,intensity_columns_list) { unique_mapped_proteins_list = unique(mapped_peptides_in$mapped_protein) # Gets list of all peptides listed. # Generates a blank data frame with clomns of Intensities and rows of Uniprots. Tukeys_df = data.frame(mapped_protein = unique_mapped_proteins_list, stringsAsFactors = FALSE ) for (q in intensity_columns_list) {Tukeys_df[,q] = NA} for (i in 1:length(unique_mapped_proteins_list)) { mapped_peptides_unique_subset = subset(mapped_peptides_in, mapped_protein == unique_mapped_proteins_list[i]) #calculate Tukey's Biweight from library(affy); returns a single numeric #results_list[[i]] = data.frame(Protein=unique_mapped_proteins_list[i],Peptides_per_protein=nrow(mapped_peptides_unique_subset)) for (j in intensity_columns_list) { #Populates with new Tukeys values. Tukeys_df[i,j] = 2^(tukey.biweight(mapped_peptides_unique_subset[,j])) } } return(Tukeys_df) } args <- commandArgs(trailingOnly = TRUE) main(args[1], args[2])