annotate pre_process_protein_name_set.R @ 32:fce21ee45396 draft

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1 #######################################################################################
2 # R-code: Protein Name and Tukey's Normalization
3 # Author: Adam L Borne
4 # Contributers: Paul A Stewart, Brent Kuenzi
5 #######################################################################################
6 # Assigns uniprot id from MaxQuant peptides file. Filters and normalizes the
7 # intensities of each proteins. Resulting in a one to one list of intensities to 
8 # uniprot id.
9 #######################################################################################
10 # Copyright (C)  Adam L Borne.
11 # Permission is granted to copy, distribute and/or modify this document
12 # under the terms of the GNU Free Documentation License, Version 1.3
13 # or any later version published by the Free Software Foundation;
14 # with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
15 # A copy of the license is included in the section entitled "GNU
16 # Free Documentation License".
17 #######################################################################################
18 ## REQUIRED INPUT ##
19 
20 # 1) peptides_file: MaxQuant peptides file. 
21 #######################################################################################
22 ins_check_run <- function() {
23   if ("affy" %in% rownames(installed.packages())){}
24   else {
25     source("https://bioconductor.org/biocLite.R")
26     biocLite(c('mygene','affy'))
27   }
28   if ('data.table' %in% rownames(installed.packages())){}
29   else {
30     install.packages('data.table', repos='http://cran.us.r-project.org')
31   }
32   if ('stringr' %in% rownames(installed.packages())){}
33   else {
34     install.packages('stringr', repos='http://cran.us.r-project.org')
35   }
36   if ('VennDiagram' %in% rownames(installed.packages())){}
37   else {
38     install.packages('VennDiagram', repos='http://cran.us.r-project.org')
39   }
40 }
41 
42 ins_check_run()
43 library(data.table)
44 library(affy)
45 library(stringr)
46 library(mygene)
47 library(VennDiagram)
48 #####
49 #data
50 
51 #We should chat a bit more about using Tukey's and handling 0's/missing values with Brent.
52 #Ask me about some updates for doing a bit more filtering of TMT data.
53 
54 main <- function(peptides_file, db_path) {
55 	peptides_file = read.delim(peptides_file,header=TRUE,stringsAsFactors=FALSE,fill=TRUE)
56   peptides_txt = peptides_file
57 	intensity_columns = names(peptides_txt[,str_detect(names(peptides_txt),"Intensity\\.*")]) #Pulls out all lines with Intensity in them.
58 	intensity_columns = intensity_columns[2:length(intensity_columns)] #Removes the first column that does not have a bait. 
59 	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.
60 	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.
61 	peptides_txt_mapped = subset(peptides_txt_mapped,!is.na(Uniprot)) #removes reverse sequences and any that didn't match a uniprot accession
62 	columns_comb = c("Uniprot", intensity_columns) 
63 	peptides_mapped_intensity = subset(peptides_txt_mapped, select = columns_comb) #Subsets out only the needed cloumns for Tukeys (Uniprot IDS and baited intensities)
64 	swissprot_fasta = scan(db_path, what="character")
65 	peptides_txt_mapped_log2 = peptides_mapped_intensity
66   # Takes the log2 of the intensities. 
67 	for (i in intensity_columns) { 
68 		peptides_txt_mapped_log2[,i] = log2(subset(peptides_txt_mapped_log2, select = i))
69 	}
70   #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 
71 	global_min = min(apply(peptides_txt_mapped_log2[,2:ncol(peptides_txt_mapped_log2)],2,function(x) {
72 	  min(x[x != -Inf])
73 	}))
74 	peptides_txt_mapped_log2[peptides_txt_mapped_log2 == -Inf] <- 0
75   #uniprot accessions WITHOUT isoforms; it looks like only contaminants contain isoforms anyways
76 	mapped_protein_uniprotonly = str_extract(peptides_txt_mapped_log2$Uniprot,"[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") 
77 	mapped_protein_uniprot_accession = str_extract(peptides_txt_mapped_log2$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}")
78 	peptides_txt_mapped_log2$mapped_protein = mapped_protein_uniprotonly
79   # Runs the Tukey function returning completed table
80   peptides_txt_mapped_log2 = subset(peptides_txt_mapped_log2,mapped_protein %in% swissprot_fasta)
81 	protein_intensities_tukeys = get_protein_values(peptides_txt_mapped_log2,intensity_columns)
82   protein_intensities_tukeys[protein_intensities_tukeys == 1] <- 0
83   write.table(protein_intensities_tukeys, "./tukeys_output.txt", row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")	
84 
85 }
86 
87 map_peptides_proteins = function(peptides_in) {
88     #reverse sequences are blank but have a razor protein indicating that they are reverse; exclude these for now
89     peptides_in = subset(peptides_in,peptides_in$Proteins != "")
90     results_list = list()
91     k = 1
92     for (i in 1:nrow(peptides_in)) {
93         protein_names = peptides_in[i,"Proteins"]
94         protein_names_split = unlist(strsplit(protein_names,";"))
95         for (j in 1:length(protein_names_split)) {
96             peptides_mapped_proteins = data.frame(peptides_in[i,],mapped_protein=protein_names_split[j],stringsAsFactors=FALSE)
97             results_list[[k]] = peptides_mapped_proteins
98             k = k+1
99             
00         }
01     }
02     return(rbindlist(results_list))
03 }
04 
05 get_protein_values = function(mapped_peptides_in,intensity_columns_list) {
06   unique_mapped_proteins_list = unique(mapped_peptides_in$mapped_protein) # Gets list of all peptides listed.
07   # Generates a blank data frame with clomns of Intensities and rows of Uniprots.
08   Tukeys_df = data.frame(mapped_protein = unique_mapped_proteins_list, stringsAsFactors = FALSE ) 
09   for (q in intensity_columns_list) {Tukeys_df[,q] = NA}
10   for (i in 1:length(unique_mapped_proteins_list)) {
11     mapped_peptides_unique_subset = subset(mapped_peptides_in, mapped_protein == unique_mapped_proteins_list[i])
12     #calculate Tukey's Biweight from library(affy); returns a single numeric
13     #results_list[[i]] = data.frame(Protein=unique_mapped_proteins_list[i],Peptides_per_protein=nrow(mapped_peptides_unique_subset))
14     for (j in intensity_columns_list) {
15       #Populates with new Tukeys values.
16       Tukeys_df[i,j] = 2^(tukey.biweight(mapped_peptides_unique_subset[,j]))
17     }
18   }
19   return(Tukeys_df)
20 }
21 
22 
23 args <- commandArgs(trailingOnly = TRUE)
24 main(args[1], args[2])