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pre_process_protein_name_set.R |
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| diff -r ddc092714127 -r 945f600f34cb pre_process_protein_name_set.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/pre_process_protein_name_set.R Tue Mar 15 15:59:16 2016 -0400 |
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| @@ -0,0 +1,128 @@ +####################################################################################### +# R-code: Protein Name and Tukey's Normalization +# Author: Adam L Borne +# Contributers: Paul A Stewart, Brent Kuenzi +####################################################################################### +# Assigns uniprot id from MaxQuant peptides file. Filters and normalizes the +# intensities of each proteins. Resulting in a one to one list of intensities to +# uniprot id. +####################################################################################### +# Copyright (C) Adam Borne. +# Permission is granted to copy, distribute and/or modify this document +# under the terms of the GNU Free Documentation License, Version 1.3 +# or any later version published by the Free Software Foundation; +# with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. +# A copy of the license is included in the section entitled "GNU +# Free Documentation License". +####################################################################################### +## REQUIRED INPUT ## + +# 1) peptides_file: MaxQuant peptides file. +####################################################################################### + + +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) + + +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$mapped_protein = mapped_protein_uniprotonly + # Runs the Tukey function returning completed table. + peptides_txt_mapped_log2 = subset(peptides_txt_mapped_log2,mapped_protein %in% swissprot_fasta) + protein_intensities_tukeys = get_protein_values(peptides_txt_mapped_log2,intensity_columns) + protein_intensities_tukeys[protein_intensities_tukeys == 1] <- 0 + write.table(protein_intensities_tukeys, "./tukeys_output.txt", row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t") + +} + +map_peptides_proteins = function(peptides_in) { + peptides_in = subset(peptides_in,peptides_in$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]) |