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