annotate pre_process_protein_name_set.R @ 15:f6a27a99e874 draft

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1 library(data.table)
2 library(affy)
3 library(stringr)
4 library(mygene)
5 library(VennDiagram)
6 #####
7 #data
8 main <- function(peptides_file) {
9 	peptides_file = read.delim(peptides_file,header=TRUE,stringsAsFactors=FALSE,fill=TRUE)
10   peptides_txt = peptides_file
11 	intensity_columns = names(peptides_txt[,str_detect(names(peptides_txt),"Intensity\\.*")]) #Pulls out all lines with Intensity in them.
12 	intensity_columns = intensity_columns[2:length(intensity_columns)] #Removes the first column that does not have a bait. 
13 	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.
14 	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.
15 	peptides_txt_mapped = subset(peptides_txt_mapped,!is.na(Uniprot)) #removes reverse sequences and any that didn't match a uniprot accession
16 	columns_comb = c("Uniprot", intensity_columns) 
17 	peptides_mapped_intensity = subset(peptides_txt_mapped, select = columns_comb) #Subsets out only the needed cloumns for Tukeys (Uniprot IDS and baited intensities)
18 	swissprot_fasta = scan("/home/philip/galaxy/tools/Moffitt_Tools/uniprot_names.txt",what="character")
19 	peptides_txt_mapped_log2 = peptides_mapped_intensity
20   # Takes the log2 of the intensities. 
21 	for (i in intensity_columns) { 
22 		peptides_txt_mapped_log2[,i] = log2(subset(peptides_txt_mapped_log2, select = i))
23 	}
24   #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 
25 	global_min = min(apply(peptides_txt_mapped_log2[,2:ncol(peptides_txt_mapped_log2)],2,function(x) {
26 	  min(x[x != -Inf])
27 	}))
28 	peptides_txt_mapped_log2[peptides_txt_mapped_log2 == -Inf] <- 0
29   #uniprot accessions WITHOUT isoforms; it looks like only contaminants contain isoforms anyways
30 	mapped_protein_uniprotonly = str_extract(peptides_txt_mapped_log2$Uniprot,"[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}") 
31 	mapped_protein_uniprot_accession = str_extract(peptides_txt_mapped_log2$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}")
32 	peptides_txt_mapped_log2$mapped_protein = mapped_protein_uniprotonly
33   # Runs the Tukey function returning completed table
34   peptides_txt_mapped_log2 = subset(peptides_txt_mapped_log2,mapped_protein %in% swissprot_fasta)
35 	protein_intensities_tukeys = get_protein_values(peptides_txt_mapped_log2,intensity_columns)
36   protein_intensities_tukeys[protein_intensities_tukeys == 1] <- 0
37   write.table(protein_intensities_tukeys, "./tukeys_output.txt", row.names = FALSE, col.names = TRUE, quote = FALSE, sep = "\t")	
38 
39 }
40 
41 map_peptides_proteins = function(peptides_in) {
42     #reverse sequences are blank but have a razor protein indicating that they are reverse; exclude these for now
43     peptides_in = subset(peptides_in,peptides_in$Proteins != "")
44     results_list = list()
45     k = 1
46     for (i in 1:nrow(peptides_in)) {
47         protein_names = peptides_in[i,"Proteins"]
48         protein_names_split = unlist(strsplit(protein_names,";"))
49         for (j in 1:length(protein_names_split)) {
50             peptides_mapped_proteins = data.frame(peptides_in[i,],mapped_protein=protein_names_split[j],stringsAsFactors=FALSE)
51             results_list[[k]] = peptides_mapped_proteins
52             k = k+1
53             
54         }
55     }
56     return(rbindlist(results_list))
57 }
58 
59 get_protein_values = function(mapped_peptides_in,intensity_columns_list) {
60   unique_mapped_proteins_list = unique(mapped_peptides_in$mapped_protein) # Gets list of all peptides listed.
61   # Generates a blank data frame with clomns of Intensities and rows of Uniprots.
62   Tukeys_df = data.frame(mapped_protein = unique_mapped_proteins_list, stringsAsFactors = FALSE ) 
63   for (q in intensity_columns_list) {Tukeys_df[,q] = NA}
64   for (i in 1:length(unique_mapped_proteins_list)) {
65     mapped_peptides_unique_subset = subset(mapped_peptides_in, mapped_protein == unique_mapped_proteins_list[i])
66     #calculate Tukey's Biweight from library(affy); returns a single numeric
67     #results_list[[i]] = data.frame(Protein=unique_mapped_proteins_list[i],Peptides_per_protein=nrow(mapped_peptides_unique_subset))
68     for (j in intensity_columns_list) {
69       #Populates with new Tukeys values.
70       Tukeys_df[i,j] = 2^(tukey.biweight(mapped_peptides_unique_subset[,j]))
71     }
72   }
73   return(Tukeys_df)
74 }
75 
76 args <- commandArgs(trailingOnly = TRUE)
77 main(args[1])