Finish R script linting
This commit is contained in:
@@ -1,9 +1,9 @@
|
||||
#!/usr/bin/env R
|
||||
#!/usr/bin/env R
|
||||
# GTA (GoTermAveraging)
|
||||
# Your output may not be reproducible as org.Sc.sgd.db is uploaded from Bioconductor R library and changes
|
||||
#
|
||||
# Updated 240724 Bryan C Roessler to improve file operations and portability
|
||||
# NOTE: The script now has 2 additional OPTIONAL arguments:
|
||||
# NOTE: The script now has 2 additional OPTIONAL arguments:
|
||||
# 1. Path to SGD terms file (go.terms.tab)
|
||||
# 2. Path to SGD features file (gene_association.sgd)
|
||||
|
||||
@@ -40,160 +40,157 @@ if (length(args) >= 5) {
|
||||
output_dir <- "../../out/gta" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd
|
||||
}
|
||||
|
||||
|
||||
|
||||
# # Set SGDgeneList file path
|
||||
# if (length(args) > 4) {
|
||||
# SGDgeneList <- args[4]
|
||||
# } else {
|
||||
# SGDgeneList <- "../Code/SGD_features.tab"
|
||||
|
||||
|
||||
#Begin for loop for experiments in this study-----------------ZScores_Interaction.csv
|
||||
for(m in 1:length(zscores_file)){
|
||||
# Begin for loop for experiments in this study
|
||||
# ZScores_Interaction.csv
|
||||
for (m in 1:length(zscores_file)) {
|
||||
|
||||
#zscores_file <- paste(Wstudy,"/",expName[m],'/ZScores/ZScores_Interaction.csv',sep="") #ArgsScore[1]
|
||||
X <- read.csv(file = zscores_file[m],stringsAsFactors=FALSE,header = TRUE)
|
||||
X <- read.csv(file = zscores_file[m], stringsAsFactors = FALSE, header = TRUE)
|
||||
|
||||
if(colnames(X)[1] == "OrfRep"){
|
||||
if (colnames(X)[1] == "OrfRep") {
|
||||
colnames(X)[1] <- "ORF"
|
||||
}
|
||||
|
||||
#Terms is the GO term list
|
||||
Terms <- read.delim(file = sgd_terms_file,header=FALSE,quote = "",col.names = c("GO_ID","GO_Term","GO_Aspect","GO_Term_Definition"))
|
||||
Terms <- read.delim(file = sgd_terms_file, header = FALSE, quote = "",
|
||||
col.names = c("GO_ID", "GO_Term", "GO_Aspect", "GO_Term_Definition"))
|
||||
#all ORFs associated with GO term
|
||||
GO2ALLORFs <- as.list(org.Sc.sgdGO2ALLORFS)
|
||||
#Gene_Association is the gene association to GO term file
|
||||
Gene_Association <- read.delim(sgd_features_file,skip=8,header=FALSE,quote="",col.names = c("Database","Database_Object_ID","Database_Object_Symbol","NOT","GO_ID","Database_Reference","Evidence","With_or_From","Aspect","Database_Object_Name","Database_Object_Synonym","Database_Object_Type","taxon","Date","Assigned_By","OtherInfo","Empty"))
|
||||
Gene_Association <- read.delim(sgd_features_file, skip = 8, header = FALSE, quote = "",
|
||||
col.names = c("Database", "Database_Object_ID", "Database_Object_Symbol", "NOT", "GO_ID",
|
||||
"Database_Reference", "Evidence", "With_or_From", "Aspect", "Database_Object_Name",
|
||||
"Database_Object_Synonym", "Database_Object_Type", "taxon", "Date", "Assigned_By", "OtherInfo", "Empty"
|
||||
)
|
||||
)
|
||||
#Get the ORF names associated with each gene/GO term
|
||||
Gene_Association$ORF <- str_split_fixed(as.character(Gene_Association$Database_Object_Synonym),"\\|",2)[,1]
|
||||
Gene_Association$ORF <- str_split_fixed(as.character(Gene_Association$Database_Object_Synonym), "\\|", 2)[, 1]
|
||||
#Get the numeric GO ID for matching
|
||||
Gene_Association$GO_ID_Numeric <- as.integer(str_split_fixed(as.character(Gene_Association$GO_ID),"\\:",2)[,2])
|
||||
#get all unique GO terms
|
||||
Gene_Association$GO_ID_Numeric <- as.integer(str_split_fixed(as.character(Gene_Association$GO_ID), "\\:", 2)[, 2])
|
||||
#Get all unique GO terms
|
||||
GO_Terms <- unique(Gene_Association$GO_ID)
|
||||
#create a character vector with just the ColNames of the input file to store the scores for each GO term
|
||||
#Create a character vector with just the ColNames of the input file to store the scores for each GO term
|
||||
Col_Names_X <- colnames(X)
|
||||
#create a data_frame with header from input_file
|
||||
GO_Term_Averages <- X[0,]
|
||||
#fill table with NAs same length as number of GO terms
|
||||
GO_Term_Averages[1:length(GO_Terms),] <- NA
|
||||
#change the first and second col names to GO_ID and Term
|
||||
#Create a data_frame with header from input_file
|
||||
GO_Term_Averages <- X[0, ]
|
||||
#Fill table with NAs same length as number of GO terms
|
||||
GO_Term_Averages[1:length(GO_Terms), ] <- NA
|
||||
#Change the first and second col names to GO_ID and Term
|
||||
colnames(GO_Term_Averages)[1] <- "GO_ID"
|
||||
colnames(GO_Term_Averages)[2] <- "Term"
|
||||
|
||||
#create new columns for Ontology, number genes (used to calculate the avg score), all possible genes in the GO term, and print genes/ORFs used
|
||||
# Create new columns
|
||||
GO_Term_Averages$Ontology <- NA
|
||||
GO_Term_Averages$NumGenes <- NA
|
||||
GO_Term_Averages$AllPossibleGenes <- NA
|
||||
GO_Term_Averages$Genes <- NA
|
||||
GO_Term_Averages$ORFs <- NA
|
||||
|
||||
#create a data.frame for the standard deviation info
|
||||
GO_Term_SD <- X[0,]
|
||||
GO_Term_SD[1:length(GO_Terms),] <- NA
|
||||
# Create a data.frame for the standard deviation info
|
||||
GO_Term_SD <- X[0, ]
|
||||
GO_Term_SD[1:length(GO_Terms), ] <- NA
|
||||
colnames(GO_Term_SD)[1] <- "GO_ID"
|
||||
colnames(GO_Term_SD)[2] <- "Term"
|
||||
|
||||
#Loop for each GO term to get an average L and K Z score
|
||||
for(i in 1:length(GO_Terms)){
|
||||
#get the GO_Term
|
||||
# Loop for each GO term to get an average L and K Z score
|
||||
for (i in 1:length(GO_Terms)) {
|
||||
# Get the GO_Term
|
||||
ID <- GO_Terms[i]
|
||||
#Get data.frame for all genes associated to the GO Term
|
||||
ID_AllGenes <- Gene_Association[Gene_Association$GO_ID == ID,]
|
||||
#get a vector of just the gene names
|
||||
# Get data.frame for all genes associated to the GO Term
|
||||
ID_AllGenes <- Gene_Association[Gene_Association$GO_ID == ID, ]
|
||||
# Get a vector of just the gene names
|
||||
ID_AllGenes_vector <- as.vector(GO2ALLORFs[as.character(ID)][[1]])
|
||||
if(length(unique(ID_AllGenes_vector)) > 4000){
|
||||
if (length(unique(ID_AllGenes_vector)) > 4000) {
|
||||
next()
|
||||
}
|
||||
#get the GO term character description where numeric Terms ID matches GO_Term's ID
|
||||
GO_Description_Term <- as.character(Terms[Terms$GO_ID %in% ID_AllGenes$GO_ID_Numeric,]$GO_Term[1])
|
||||
#get the Z scores for all genes in the GO_ID
|
||||
Zscores_For_ID <- X[X$ORF %in% ID_AllGenes_vector,]
|
||||
#get the Gene names and ORFs for the term
|
||||
GO_Term_Averages$Genes[i] <- paste(unique(Zscores_For_ID$Gene),collapse=" | ")
|
||||
GO_Term_Averages$ORFs[i] <- paste(unique(Zscores_For_ID$ORF),collapse=" | ")
|
||||
# Get the GO term character description where numeric Terms ID matches GO_Term's ID
|
||||
GO_Description_Term <- as.character(Terms[Terms$GO_ID %in% ID_AllGenes$GO_ID_Numeric, ]$GO_Term[1])
|
||||
# Get the Z scores for all genes in the GO_ID
|
||||
Zscores_For_ID <- X[X$ORF %in% ID_AllGenes_vector, ]
|
||||
# Get the Gene names and ORFs for the term
|
||||
GO_Term_Averages$Genes[i] <- paste(unique(Zscores_For_ID$Gene), collapse = " | ")
|
||||
GO_Term_Averages$ORFs[i] <- paste(unique(Zscores_For_ID$ORF), collapse = " | ")
|
||||
|
||||
#dataframe to report the averages for a GO term
|
||||
#get the GO ID
|
||||
# Dataframe to report the averages for a GO term
|
||||
# Get the GO ID
|
||||
GO_Term_Averages$GO_ID[i] <- as.character(ID)
|
||||
#get the term name
|
||||
# Get the term name
|
||||
GO_Term_Averages$Term[i] <- GO_Description_Term
|
||||
#get total number of genes annotated to the Term that we have in our library
|
||||
# Get total number of genes annotated to the Term that we have in our library
|
||||
GO_Term_Averages$NumGenes[i] <- length(unique(Zscores_For_ID$ORF))
|
||||
#get total number of genes annotated to the Term in SGD
|
||||
# Get total number of genes annotated to the Term in SGD
|
||||
GO_Term_Averages$AllPossibleGenes[i] <- length(unique(ID_AllGenes_vector))
|
||||
#get the ontology of the term
|
||||
# Get the ontology of the term
|
||||
GO_Term_Averages$Ontology[i] <- as.character(ID_AllGenes$Aspect[1])
|
||||
|
||||
#calculate the average score for every column
|
||||
for(j in 3:length(X[1,])){
|
||||
GO_Term_Averages[i,j] <- mean(Zscores_For_ID[,j],na.rm = TRUE)
|
||||
#GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
|
||||
# Calculate the average score for every column
|
||||
for (j in 3:length(X[1, ])) {
|
||||
GO_Term_Averages[i, j] <- mean(Zscores_For_ID[, j], na.rm = TRUE)
|
||||
# GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
|
||||
}
|
||||
|
||||
#also calculate same values for the SD
|
||||
# Also calculate same values for the SD
|
||||
GO_Term_SD$GO_ID[i] <- as.character(ID)
|
||||
#get the term name
|
||||
# Get the term name
|
||||
GO_Term_SD$Term[i] <- GO_Description_Term
|
||||
|
||||
#calculate column scores for SD
|
||||
for(j in 3:length(X[1,])){
|
||||
GO_Term_SD[i,j] <- sd(Zscores_For_ID[,j],na.rm = TRUE)
|
||||
#GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
|
||||
# Calculate column scores for SD
|
||||
for (j in 3:length(X[1, ])) {
|
||||
GO_Term_SD[i, j] <- sd(Zscores_For_ID[, j], na.rm = TRUE)
|
||||
# GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])])
|
||||
}
|
||||
}
|
||||
#add either _Avg or _SD depending on if the calculated score is an average or SD
|
||||
colnames(GO_Term_Averages) <- paste(colnames(GO_Term_Averages),"Avg", sep = "_")
|
||||
colnames(GO_Term_SD) <- paste(colnames(GO_Term_SD),"SD", sep = "_")
|
||||
#combine the averages with the SDs to make one big data.frame
|
||||
X2 <- cbind(GO_Term_Averages,GO_Term_SD)
|
||||
#test[ , order(names(test))]
|
||||
X2 <- X2[,order(names(X2))]
|
||||
X2 <- X2[!is.na(X2$Z_lm_L_Avg),]
|
||||
#create output file
|
||||
write.csv(X2,file=paste(output_dir,"/",expName[m],"/Average_GOTerms_All.csv",sep=""),row.names=FALSE)
|
||||
#remove NAs
|
||||
X3 <- X2[!is.na(X2$Z_lm_L_Avg),]
|
||||
#identify redundant GO terms
|
||||
# Add either _Avg or _SD depending on if the calculated score is an average or SD
|
||||
colnames(GO_Term_Averages) <- paste(colnames(GO_Term_Averages), "Avg", sep = "_")
|
||||
colnames(GO_Term_SD) <- paste(colnames(GO_Term_SD), "SD", sep = "_")
|
||||
# Combine the averages with the SDs to make one big data.frame
|
||||
X2 <- cbind(GO_Term_Averages, GO_Term_SD)
|
||||
# Test[ , order(names(test))]
|
||||
X2 <- X2[, order(names(X2))]
|
||||
X2 <- X2[!is.na(X2$Z_lm_L_Avg), ]
|
||||
# Create output file
|
||||
write.csv(X2, file.path(output_dir, expName[m], "Average_GOTerms_All.csv"), row.names = FALSE)
|
||||
# Remove NAs
|
||||
X3 <- X2[!is.na(X2$Z_lm_L_Avg), ]
|
||||
|
||||
for(i in 1:length(X3[,1])){
|
||||
#loop through each GO term - get term
|
||||
# Identify redundant GO terms
|
||||
for (i in 1:length(X3[, 1])) {
|
||||
# Loop through each GO term - get term
|
||||
GO_term_ID <- as.character(X3$GO_ID_Avg[i])
|
||||
#get term in the X3
|
||||
X3_Temp <- X3[X3$GO_ID_Avg == GO_term_ID,]
|
||||
#get anywhere that has the same number K_Avg value
|
||||
X3_Temp2 <- X3[X3$Z_lm_K_Avg %in% X3_Temp,]
|
||||
if(length(X3_Temp2[,1]) > 1){
|
||||
if(length(unique(X3_Temp2$Genes_Avg)) == 1){
|
||||
X3_Temp2 <- X3_Temp2[1,]
|
||||
# Get term in the X3
|
||||
X3_Temp <- X3[X3$GO_ID_Avg == GO_term_ID, ]
|
||||
# Get anywhere that has the same number K_Avg value
|
||||
X3_Temp2 <- X3[X3$Z_lm_K_Avg %in% X3_Temp, ]
|
||||
if (length(X3_Temp2[, 1]) > 1) {
|
||||
if (length(unique(X3_Temp2$Genes_Avg)) == 1) {
|
||||
X3_Temp2 <- X3_Temp2[1, ]
|
||||
}
|
||||
}
|
||||
|
||||
if(i == 1){
|
||||
if (i == 1) {
|
||||
Y <- X3_Temp2
|
||||
}
|
||||
if(i > 1){
|
||||
Y <- rbind(Y,X3_Temp2)
|
||||
if (i > 1) {
|
||||
Y <- rbind(Y, X3_Temp2)
|
||||
}
|
||||
}
|
||||
Y1 <- unique(Y)
|
||||
|
||||
write.csv(Y1,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_All_NonRedundantTerms.csv",sep=""),row.names = FALSE)
|
||||
|
||||
Y2 <- Y1[Y1$Z_lm_L_Avg >= 2 | Y1$Z_lm_L_Avg <= -2,]
|
||||
Y2 <- Y2[!is.na(Y2$Z_lm_L_Avg),]
|
||||
write.csv(Y2,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_L.csv",sep=""),row.names = FALSE)
|
||||
|
||||
Y3 <- Y2[Y2$NumGenes_Avg > 2,]
|
||||
write.csv(Y3,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_L_Above2Genes.csv",sep=""),row.names = FALSE)
|
||||
|
||||
Y4 <- Y1[Y1$Z_lm_K_Avg >= 2 | Y1$Z_lm_K_Avg <= -2,]
|
||||
Y4 <- Y4[!is.na(Y4$Z_lm_K_Avg),]
|
||||
write.csv(Y4,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_K.csv",sep=""),row.names = FALSE)
|
||||
|
||||
Y5 <- Y4[Y4$NumGenes_Avg > 2,]
|
||||
write.csv(Y5,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_K_Above2Genes.csv",sep=""),row.names = FALSE)
|
||||
|
||||
#End of 'for loop'
|
||||
}
|
||||
|
||||
Y1 <- unique(Y)
|
||||
write.csv(Y1, file.path(output_dir, exp_name, "Average_GOTerms_All_NonRedundantTerms.csv"), row.names = FALSE)
|
||||
Y2 <- Y1[Y1$Z_lm_L_Avg >= 2 | Y1$Z_lm_L_Avg <= -2, ]
|
||||
Y2 <- Y2[!is.na(Y2$Z_lm_L_Avg), ]
|
||||
write.csv(Y2, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L.csv"), row.names = FALSE)
|
||||
Y3 <- Y2[Y2$NumGenes_Avg > 2, ]
|
||||
write.csv(Y3, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L_Above2Genes.csv"), row.names = FALSE)
|
||||
Y4 <- Y1[Y1$Z_lm_K_Avg >= 2 | Y1$Z_lm_K_Avg <= -2, ]
|
||||
Y4 <- Y4[!is.na(Y4$Z_lm_K_Avg), ]
|
||||
write.csv(Y4, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K.csv"), row.names = FALSE)
|
||||
Y5 <- Y4[Y4$NumGenes_Avg > 2, ]
|
||||
write.csv(Y5, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K_Above2Genes.csv"), row.names = FALSE)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user