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qhtcp-workflow/apps/r/gtaTemplate.R
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196
qhtcp-workflow/apps/r/gtaTemplate.R
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#!/usr/bin/env R
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# GTA (GoTermAveraging)
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# Your output may not be reproducible as org.Sc.sgd.db is uploaded from Bioconductor R library and changes
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#
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# Updated 240724 Bryan C Roessler to improve file operations and portability
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# NOTE: The script now has 2 additional OPTIONAL arguments:
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# 1. Path to SGD terms file (go.terms.tab)
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# 2. Path to SGD features file (gene_association.sgd)
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library("stringr")
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library("org.Sc.sgd.db")
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library("plyr")
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# Parse arguments
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args <- commandArgs(TRUE)
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exp_name <- args[1]
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if (length(args) >= 2) {
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zscores_file <- args[2]
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} else {
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zscores_file <- "zscores/zscores_interaction.csv" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd
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}
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if (length(args) >= 3) {
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sgd_terms_file <- args[3]
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} else {
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sgd_terms_file <- "go_terms.tab"
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}
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if (length(args) >= 4) {
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sgd_features_file <- args[4]
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} else {
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sgd_features_file <- "gene_association.sgd" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd
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}
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if (length(args) >= 5) {
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output_dir <- args[5]
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} else {
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output_dir <- "../../out/gta" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd
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}
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# # Set SGDgeneList file path
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# if (length(args) > 4) {
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# SGDgeneList <- args[4]
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# } else {
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# SGDgeneList <- "../Code/SGD_features.tab"
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# Begin for loop for experiments in this study
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# ZScores_Interaction.csv
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for (m in 1:length(zscores_file)) {
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# zscores_file <- paste(Wstudy,"/",expName[m],'/ZScores/ZScores_Interaction.csv',sep="") #ArgsScore[1]
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X <- read.csv(file = zscores_file[m], stringsAsFactors = FALSE, header = TRUE)
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if (colnames(X)[1] == "OrfRep") {
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colnames(X)[1] <- "ORF"
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}
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#Terms is the GO term list
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Terms <- read.delim(file = sgd_terms_file, header = FALSE, quote = "",
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col.names = c("GO_ID", "GO_Term", "GO_Aspect", "GO_Term_Definition"))
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#all ORFs associated with GO term
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GO2ALLORFs <- as.list(org.Sc.sgdGO2ALLORFS)
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#Gene_Association is the gene association to GO term file
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Gene_Association <- read.delim(sgd_features_file, skip = 8, header = FALSE, quote = "",
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col.names = c("Database", "Database_Object_ID", "Database_Object_Symbol", "NOT", "GO_ID",
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"Database_Reference", "Evidence", "With_or_From", "Aspect", "Database_Object_Name",
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"Database_Object_Synonym", "Database_Object_Type", "taxon", "Date", "Assigned_By", "OtherInfo", "Empty"
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)
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)
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#Get the ORF names associated with each gene/GO term
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Gene_Association$ORF <- str_split_fixed(as.character(Gene_Association$Database_Object_Synonym), "\\|", 2)[, 1]
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#Get the numeric GO ID for matching
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Gene_Association$GO_ID_Numeric <- as.integer(str_split_fixed(as.character(Gene_Association$GO_ID), "\\:", 2)[, 2])
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#Get all unique GO terms
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GO_Terms <- unique(Gene_Association$GO_ID)
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#Create a character vector with just the ColNames of the input file to store the scores for each GO term
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Col_Names_X <- colnames(X)
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#Create a data_frame with header from input_file
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GO_Term_Averages <- X[0, ]
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#Fill table with NAs same length as number of GO terms
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GO_Term_Averages[1:length(GO_Terms), ] <- NA
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#Change the first and second col names to GO_ID and Term
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colnames(GO_Term_Averages)[1] <- "GO_ID"
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colnames(GO_Term_Averages)[2] <- "Term"
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# Create new columns
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GO_Term_Averages$Ontology <- NA
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GO_Term_Averages$NumGenes <- NA
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GO_Term_Averages$AllPossibleGenes <- NA
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GO_Term_Averages$Genes <- NA
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GO_Term_Averages$ORFs <- NA
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# Create a data.frame for the standard deviation info
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GO_Term_SD <- X[0, ]
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GO_Term_SD[1:length(GO_Terms), ] <- NA
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colnames(GO_Term_SD)[1] <- "GO_ID"
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colnames(GO_Term_SD)[2] <- "Term"
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# Loop for each GO term to get an average L and K Z score
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for (i in 1:length(GO_Terms)) {
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# Get the GO_Term
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ID <- GO_Terms[i]
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# Get data.frame for all genes associated to the GO Term
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ID_AllGenes <- Gene_Association[Gene_Association$GO_ID == ID, ]
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# Get a vector of just the gene names
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ID_AllGenes_vector <- as.vector(GO2ALLORFs[as.character(ID)][[1]])
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if (length(unique(ID_AllGenes_vector)) > 4000) {
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next()
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}
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# Get the GO term character description where numeric Terms ID matches GO_Term's ID
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GO_Description_Term <- as.character(Terms[Terms$GO_ID %in% ID_AllGenes$GO_ID_Numeric, ]$GO_Term[1])
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# Get the Z scores for all genes in the GO_ID
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Zscores_For_ID <- X[X$ORF %in% ID_AllGenes_vector, ]
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# Get the Gene names and ORFs for the term
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GO_Term_Averages$Genes[i] <- paste(unique(Zscores_For_ID$Gene), collapse = " | ")
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GO_Term_Averages$ORFs[i] <- paste(unique(Zscores_For_ID$ORF), collapse = " | ")
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# Dataframe to report the averages for a GO term
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# Get the GO ID
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GO_Term_Averages$GO_ID[i] <- as.character(ID)
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# Get the term name
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GO_Term_Averages$Term[i] <- GO_Description_Term
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# Get total number of genes annotated to the Term that we have in our library
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GO_Term_Averages$NumGenes[i] <- length(unique(Zscores_For_ID$ORF))
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# Get total number of genes annotated to the Term in SGD
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GO_Term_Averages$AllPossibleGenes[i] <- length(unique(ID_AllGenes_vector))
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# Get the ontology of the term
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GO_Term_Averages$Ontology[i] <- as.character(ID_AllGenes$Aspect[1])
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# Calculate the average score for every column
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for (j in 3:length(X[1, ])) {
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GO_Term_Averages[i, j] <- mean(Zscores_For_ID[, j], na.rm = TRUE)
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# GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
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}
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# Also calculate same values for the SD
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GO_Term_SD$GO_ID[i] <- as.character(ID)
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# Get the term name
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GO_Term_SD$Term[i] <- GO_Description_Term
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# Calculate column scores for SD
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for (j in 3:length(X[1, ])) {
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GO_Term_SD[i, j] <- sd(Zscores_For_ID[, j], na.rm = TRUE)
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# GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
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}
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}
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# Add either _Avg or _SD depending on if the calculated score is an average or SD
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colnames(GO_Term_Averages) <- paste(colnames(GO_Term_Averages), "Avg", sep = "_")
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colnames(GO_Term_SD) <- paste(colnames(GO_Term_SD), "SD", sep = "_")
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# Combine the averages with the SDs to make one big data.frame
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X2 <- cbind(GO_Term_Averages, GO_Term_SD)
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# Test[ , order(names(test))]
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X2 <- X2[, order(names(X2))]
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X2 <- X2[!is.na(X2$Z_lm_L_Avg), ]
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# Create output file
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write.csv(X2, file.path(output_dir, expName[m], "Average_GOTerms_All.csv"), row.names = FALSE)
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# Remove NAs
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X3 <- X2[!is.na(X2$Z_lm_L_Avg), ]
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# Identify redundant GO terms
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for (i in 1:length(X3[, 1])) {
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# Loop through each GO term - get term
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GO_term_ID <- as.character(X3$GO_ID_Avg[i])
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# Get term in the X3
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X3_Temp <- X3[X3$GO_ID_Avg == GO_term_ID, ]
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# Get anywhere that has the same number K_Avg value
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X3_Temp2 <- X3[X3$Z_lm_K_Avg %in% X3_Temp, ]
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if (length(X3_Temp2[, 1]) > 1) {
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if (length(unique(X3_Temp2$Genes_Avg)) == 1) {
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X3_Temp2 <- X3_Temp2[1, ]
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}
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}
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if (i == 1) {
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Y <- X3_Temp2
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}
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if (i > 1) {
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Y <- rbind(Y, X3_Temp2)
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}
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}
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Y1 <- unique(Y)
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write.csv(Y1, file.path(output_dir, exp_name, "Average_GOTerms_All_NonRedundantTerms.csv"), row.names = FALSE)
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Y2 <- Y1[Y1$Z_lm_L_Avg >= 2 | Y1$Z_lm_L_Avg <= -2, ]
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Y2 <- Y2[!is.na(Y2$Z_lm_L_Avg), ]
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write.csv(Y2, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L.csv"), row.names = FALSE)
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Y3 <- Y2[Y2$NumGenes_Avg > 2, ]
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write.csv(Y3, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L_Above2Genes.csv"), row.names = FALSE)
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Y4 <- Y1[Y1$Z_lm_K_Avg >= 2 | Y1$Z_lm_K_Avg <= -2, ]
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Y4 <- Y4[!is.na(Y4$Z_lm_K_Avg), ]
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write.csv(Y4, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K.csv"), row.names = FALSE)
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Y5 <- Y4[Y4$NumGenes_Avg > 2, ]
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write.csv(Y5, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K_Above2Genes.csv"), row.names = FALSE)
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}
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