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Finish R script linting

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Bryan Roessler
2024-08-13 21:32:56 -04:00
parent 79862ddab4
commit 1ba1f14537
7 changed files with 1559 additions and 1211 deletions
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1
workflow/apps/r/CompileGTF.R
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@@ -52,4 +52,3 @@ output_file <- file.path(outDir, "GTFCombined.xlsx")
# Call the function to combine the files into a workbook with named sheets # Call the function to combine the files into a workbook with named sheets
combineFilesToWorkbook(file_list, output_file) combineFilesToWorkbook(file_list, output_file)

1091
workflow/apps/r/PairwiseLK.R
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873
workflow/apps/r/TSHeatmaps5dev2.R
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189
workflow/apps/r/gtaTemplate.R
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@@ -40,160 +40,157 @@ if (length(args) >= 5) {
output_dir <- "../../out/gta" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd output_dir <- "../../out/gta" # https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd
} }
# # Set SGDgeneList file path # # Set SGDgeneList file path
# if (length(args) > 4) { # if (length(args) > 4) {
# SGDgeneList <- args[4] # SGDgeneList <- args[4]
# } else { # } else {
# SGDgeneList <- "../Code/SGD_features.tab" # SGDgeneList <- "../Code/SGD_features.tab"
# Begin for loop for experiments in this study
#Begin for loop for experiments in this study-----------------ZScores_Interaction.csv # ZScores_Interaction.csv
for(m in 1:length(zscores_file)){ for (m in 1:length(zscores_file)) {
#zscores_file <- paste(Wstudy,"/",expName[m],'/ZScores/ZScores_Interaction.csv',sep="") #ArgsScore[1] #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" colnames(X)[1] <- "ORF"
} }
#Terms is the GO term list #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 #all ORFs associated with GO term
GO2ALLORFs <- as.list(org.Sc.sgdGO2ALLORFS) GO2ALLORFs <- as.list(org.Sc.sgdGO2ALLORFS)
#Gene_Association is the gene association to GO term file #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 #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 #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]) Gene_Association$GO_ID_Numeric <- as.integer(str_split_fixed(as.character(Gene_Association$GO_ID), "\\:", 2)[, 2])
#get all unique GO terms #Get all unique GO terms
GO_Terms <- unique(Gene_Association$GO_ID) GO_Terms <- unique(Gene_Association$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) Col_Names_X <- colnames(X)
#create a data_frame with header from input_file #Create a data_frame with header from input_file
GO_Term_Averages <- X[0,] GO_Term_Averages <- X[0, ]
#fill table with NAs same length as number of GO terms #Fill table with NAs same length as number of GO terms
GO_Term_Averages[1:length(GO_Terms),] <- NA GO_Term_Averages[1:length(GO_Terms), ] <- NA
#change the first and second col names to GO_ID and Term #Change the first and second col names to GO_ID and Term
colnames(GO_Term_Averages)[1] <- "GO_ID" colnames(GO_Term_Averages)[1] <- "GO_ID"
colnames(GO_Term_Averages)[2] <- "Term" 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$Ontology <- NA GO_Term_Averages$Ontology <- NA
GO_Term_Averages$NumGenes <- NA GO_Term_Averages$NumGenes <- NA
GO_Term_Averages$AllPossibleGenes <- NA GO_Term_Averages$AllPossibleGenes <- NA
GO_Term_Averages$Genes <- NA GO_Term_Averages$Genes <- NA
GO_Term_Averages$ORFs <- NA GO_Term_Averages$ORFs <- NA
#create a data.frame for the standard deviation info # Create a data.frame for the standard deviation info
GO_Term_SD <- X[0,] GO_Term_SD <- X[0, ]
GO_Term_SD[1:length(GO_Terms),] <- NA GO_Term_SD[1:length(GO_Terms), ] <- NA
colnames(GO_Term_SD)[1] <- "GO_ID" colnames(GO_Term_SD)[1] <- "GO_ID"
colnames(GO_Term_SD)[2] <- "Term" colnames(GO_Term_SD)[2] <- "Term"
#Loop for each GO term to get an average L and K Z score # Loop for each GO term to get an average L and K Z score
for(i in 1:length(GO_Terms)){ for (i in 1:length(GO_Terms)) {
#get the GO_Term # Get the GO_Term
ID <- GO_Terms[i] ID <- GO_Terms[i]
#Get data.frame for all genes associated to the GO Term # Get data.frame for all genes associated to the GO Term
ID_AllGenes <- Gene_Association[Gene_Association$GO_ID == ID,] ID_AllGenes <- Gene_Association[Gene_Association$GO_ID == ID, ]
#get a vector of just the gene names # Get a vector of just the gene names
ID_AllGenes_vector <- as.vector(GO2ALLORFs[as.character(ID)][[1]]) 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() next()
} }
#get the GO term character description where numeric Terms ID matches GO_Term's ID # 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,]$GO_Term[1]) GO_Description_Term <- as.character(Terms[Terms$GO_ID %in% ID_AllGenes$GO_ID_Numeric, ]$GO_Term[1])
#get the Z scores for all genes in the GO_ID # Get the Z scores for all genes in the GO_ID
Zscores_For_ID <- X[X$ORF %in% ID_AllGenes_vector,] Zscores_For_ID <- X[X$ORF %in% ID_AllGenes_vector, ]
#get the Gene names and ORFs for the term # Get the Gene names and ORFs for the term
GO_Term_Averages$Genes[i] <- paste(unique(Zscores_For_ID$Gene),collapse=" | ") GO_Term_Averages$Genes[i] <- paste(unique(Zscores_For_ID$Gene), collapse = " | ")
GO_Term_Averages$ORFs[i] <- paste(unique(Zscores_For_ID$ORF),collapse=" | ") GO_Term_Averages$ORFs[i] <- paste(unique(Zscores_For_ID$ORF), collapse = " | ")
#dataframe to report the averages for a GO term # Dataframe to report the averages for a GO term
#get the GO ID # Get the GO ID
GO_Term_Averages$GO_ID[i] <- as.character(ID) GO_Term_Averages$GO_ID[i] <- as.character(ID)
#get the term name # Get the term name
GO_Term_Averages$Term[i] <- GO_Description_Term GO_Term_Averages$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)) 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$AllPossibleGenes[i] <- length(unique(ID_AllGenes_vector)) GO_Term_Averages$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]) GO_Term_Averages$Ontology[i] <- as.character(ID_AllGenes$Aspect[1])
#calculate the average score for every column # Calculate the average score for every column
for(j in 3:length(X[1,])){ for (j in 3:length(X[1, ])) {
GO_Term_Averages[i,j] <- mean(Zscores_For_ID[,j],na.rm = TRUE) GO_Term_Averages[i, j] <- mean(Zscores_For_ID[, j], na.rm = TRUE)
#GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])]) # 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$GO_ID[i] <- as.character(ID) GO_Term_SD$GO_ID[i] <- as.character(ID)
#get the term name # Get the term name
GO_Term_SD$Term[i] <- GO_Description_Term GO_Term_SD$Term[i] <- GO_Description_Term
#calculate column scores for SD # Calculate column scores for SD
for(j in 3:length(X[1,])){ for (j in 3:length(X[1, ])) {
GO_Term_SD[i,j] <- sd(Zscores_For_ID[,j],na.rm = TRUE) GO_Term_SD[i, j] <- sd(Zscores_For_ID[, j], na.rm = TRUE)
#GO_Scores <- colMeans(Zscores_For_ID[,3:length(X[1,])]) # 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 # 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_Averages) <- paste(colnames(GO_Term_Averages), "Avg", sep = "_")
colnames(GO_Term_SD) <- paste(colnames(GO_Term_SD),"SD", sep = "_") colnames(GO_Term_SD) <- paste(colnames(GO_Term_SD), "SD", sep = "_")
#combine the averages with the SDs to make one big data.frame # Combine the averages with the SDs to make one big data.frame
X2 <- cbind(GO_Term_Averages,GO_Term_SD) X2 <- cbind(GO_Term_Averages, GO_Term_SD)
#test[ , order(names(test))] # Test[ , order(names(test))]
X2 <- X2[,order(names(X2))] X2 <- X2[, order(names(X2))]
X2 <- X2[!is.na(X2$Z_lm_L_Avg),] X2 <- X2[!is.na(X2$Z_lm_L_Avg), ]
#create output file # Create output file
write.csv(X2,file=paste(output_dir,"/",expName[m],"/Average_GOTerms_All.csv",sep=""),row.names=FALSE) write.csv(X2, file.path(output_dir, expName[m], "Average_GOTerms_All.csv"), row.names = FALSE)
#remove NAs # Remove NAs
X3 <- X2[!is.na(X2$Z_lm_L_Avg),] X3 <- X2[!is.na(X2$Z_lm_L_Avg), ]
#identify redundant GO terms
for(i in 1:length(X3[,1])){ # Identify redundant GO terms
#loop through each GO term - get term for (i in 1:length(X3[, 1])) {
# Loop through each GO term - get term
GO_term_ID <- as.character(X3$GO_ID_Avg[i]) GO_term_ID <- as.character(X3$GO_ID_Avg[i])
#get term in the X3 # Get term in the X3
X3_Temp <- X3[X3$GO_ID_Avg == GO_term_ID,] X3_Temp <- X3[X3$GO_ID_Avg == GO_term_ID, ]
#get anywhere that has the same number K_Avg value # Get anywhere that has the same number K_Avg value
X3_Temp2 <- X3[X3$Z_lm_K_Avg %in% X3_Temp,] X3_Temp2 <- X3[X3$Z_lm_K_Avg %in% X3_Temp, ]
if(length(X3_Temp2[,1]) > 1){ if (length(X3_Temp2[, 1]) > 1) {
if(length(unique(X3_Temp2$Genes_Avg)) == 1){ if (length(unique(X3_Temp2$Genes_Avg)) == 1) {
X3_Temp2 <- X3_Temp2[1,] X3_Temp2 <- X3_Temp2[1, ]
} }
} }
if(i == 1){ if (i == 1) {
Y <- X3_Temp2 Y <- X3_Temp2
} }
if(i > 1){ if (i > 1) {
Y <- rbind(Y,X3_Temp2) Y <- rbind(Y, X3_Temp2)
} }
} }
Y1 <- unique(Y) Y1 <- unique(Y)
write.csv(Y1, file.path(output_dir, exp_name, "Average_GOTerms_All_NonRedundantTerms.csv"), row.names = FALSE)
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$Z_lm_L_Avg), ]
Y2 <- Y1[Y1$Z_lm_L_Avg >= 2 | Y1$Z_lm_L_Avg <= -2,] write.csv(Y2, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L.csv"), row.names = FALSE)
Y2 <- Y2[!is.na(Y2$Z_lm_L_Avg),] Y3 <- Y2[Y2$NumGenes_Avg > 2, ]
write.csv(Y2,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_L.csv",sep=""),row.names = FALSE) write.csv(Y3, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_L_Above2Genes.csv"), row.names = FALSE)
Y4 <- Y1[Y1$Z_lm_K_Avg >= 2 | Y1$Z_lm_K_Avg <= -2, ]
Y3 <- Y2[Y2$NumGenes_Avg > 2,] Y4 <- Y4[!is.na(Y4$Z_lm_K_Avg), ]
write.csv(Y3,file=paste(output_dir,"/",exp_name,"/Average_GOTerms_NonRedundantTerms_Above2SD_L_Above2Genes.csv",sep=""),row.names = FALSE) write.csv(Y4, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K.csv"), row.names = FALSE)
Y5 <- Y4[Y4$NumGenes_Avg > 2, ]
Y4 <- Y1[Y1$Z_lm_K_Avg >= 2 | Y1$Z_lm_K_Avg <= -2,] write.csv(Y5, file.path(output_dir, exp_name, "Average_GOTerms_NonRedundantTerms_Above2SD_K_Above2Genes.csv"), row.names = FALSE)
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'
} }

297
workflow/apps/r/joinInteractExps.R
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@@ -9,7 +9,7 @@ args <- commandArgs(TRUE)
# Set output dir # Set output dir
if (length(args) >= 1) { if (length(args) >= 1) {
outDir <- args[1] outDir <- file.path(args[1])
} else { } else {
outDir <- "./" # for legacy workflow outDir <- "./" # for legacy workflow
} }
@@ -20,11 +20,11 @@ if (length(args) >= 2) {
} else { } else {
sd <- 2 # default value sd <- 2 # default value
} }
print(paste("SD=",sd)) print(paste("SD=", sd))
# Set studyInfo file # Set studyInfo file
if (length(args) >= 3) { if (length(args) >= 3) {
studyInfo <- args[3] studyInfo <- file.path(args[3])
} else { } else {
studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow
} }
@@ -32,7 +32,7 @@ if (length(args) >= 3) {
studies <- args[3:length(args)] studies <- args[3:length(args)]
inputFiles <- c() inputFiles <- c()
for (study in 1:length(studies)) { for (study in 1:length(studies)) {
zsFile <- file.path(study, 'zscores', 'zscores_interaction.csv') zsFile <- file.path(study, "zscores", "zscores_interaction.csv")
if (file.exists(zsFile)) { if (file.exists(zsFile)) {
inputFiles[study] <- zsFile inputFiles[study] <- zsFile
} }
@@ -43,186 +43,191 @@ print(length(inputFiles))
# TODO this is better handled in a loop in case you want to compare more experiments? # TODO this is better handled in a loop in case you want to compare more experiments?
# The input is already designed for this # The input is already designed for this
# Read in the files for your experiment and # Read in the files for your experiment and
# Join the two files at a time as a function of how many inputFile, list the larger file first ? in this example X2 has the larger number of genes. # Join the two files at a time as a function of how many inputFile
# list the larger file first ? in this example X2 has the larger number of genes
# If X1 has a larger number of genes, switch the order of X1 and X2 # If X1 has a larger number of genes, switch the order of X1 and X2
if(length(inputFiles)==2) { if (length(inputFiles) == 2) {
X1 <- read.csv(file=inputFiles[1],stringsAsFactors=FALSE) X1 <- read.csv(file = inputFiles[1], stringsAsFactors = FALSE)
X2 <- read.csv(file=inputFiles[2],stringsAsFactors=FALSE) X2 <- read.csv(file = inputFiles[2], stringsAsFactors = FALSE)
X <- join(X1,X2,by="OrfRep") X <- join(X1, X2, by = "OrfRep")
OBH=X[,order(colnames(X))] #OrderByHeader OBH <- X[, order(colnames(X))] # OrderByHeader
headSel=select(OBH, contains('OrfRep'), matches('Gene'), contains('Z_lm_K'), contains('Z_Shift_K'),contains('Z_lm_L'), contains('Z_Shift_L')) headSel <- select(OBH, contains("OrfRep"), matches("Gene"),
headSel=select(headSel, -'Gene.1') #remove 'Gene.1 column contains("Z_lm_K"), contains("Z_Shift_K"), contains("Z_lm_L"), contains("Z_Shift_L"))
headSel2=select(OBH, contains('OrfRep'), matches('Gene')) #Frame for interleaving Z_lm with Shift colums headSel <- select(headSel, -"Gene.1") #remove "Gene.1 column
headSel2=select(headSel2, -'Gene.1') #remove 'Gene.1 column #Frame for interleaving Z_lm with Shift colums headSel2 <- select(OBH, contains("OrfRep"), matches("Gene")) #Frame for interleaving Z_lm with Shift colums
headSel2 <- select(headSel2, -"Gene.1") #remove "Gene.1 column #Frame for interleaving Z_lm with Shift colums
} else if (length(inputFiles) == 3) {
X1 <- read.csv(file = inputFiles[1], stringsAsFactors = FALSE) #exp1File,stringsAsFactors = FALSE)
X2 <- read.csv(file = inputFiles[2], stringsAsFactors = FALSE) #exp2File,stringsAsFactors = FALSE)
X3 <- read.csv(file = inputFiles[3], stringsAsFactors = FALSE) #exp3File,stringsAsFactors = FALSE)
X <- join(X1, X2, by = "OrfRep")
X <- join(X, X3, by = "OrfRep")
OBH <- X[, order(colnames(X))] #OrderByHeader
headSel <- select(OBH, contains("OrfRep"), matches("Gene"),
contains("Z_lm_K"), contains("Z_Shift_K"), contains("Z_lm_L"), contains("Z_Shift_L"))
headSel <- select(headSel, -"Gene.1", -"Gene.2")
headSel2 <- select(OBH, contains("OrfRep"), matches("Gene"))
headSel2 <- select(headSel2, -"Gene.1", -"Gene.2")
}else if(length(inputFiles)==3){ } else if (length(inputFiles) == 4) {
X1 <- read.csv(file=inputFiles[1],stringsAsFactors=FALSE) #exp1File,stringsAsFactors=FALSE) X1 <- read.csv(file = inputFiles[1], stringsAsFactors = FALSE) #exp1File,stringsAsFactors = FALSE)
X2 <- read.csv(file=inputFiles[2],stringsAsFactors=FALSE) #exp2File,stringsAsFactors=FALSE) X2 <- read.csv(file = inputFiles[2], stringsAsFactors = FALSE) #exp2File,stringsAsFactors = FALSE)
X3 <- read.csv(file=inputFiles[3],stringsAsFactors=FALSE) #exp3File,stringsAsFactors=FALSE) X3 <- read.csv(file = inputFiles[3], stringsAsFactors = FALSE) #exp3File,stringsAsFactors = FALSE)
X <- join(X1,X2,by="OrfRep") X4 <- read.csv(file = inputFiles[4], stringsAsFactors = FALSE) #exp4File,stringsAsFactors = FALSE)
X <- join(X,X3,by="OrfRep") X <- join(X1, X2, by = "OrfRep")
OBH=X[,order(colnames(X))] #OrderByHeader X <- join(X, X3, by = "OrfRep")
headSel=select(OBH, contains('OrfRep'), matches('Gene'), contains('Z_lm_K'), contains('Z_Shift_K'),contains('Z_lm_L'), contains('Z_Shift_L')) X <- join(X, X4, by = "OrfRep")
headSel=select(headSel, -'Gene.1',-'Gene.2') OBH <- X[, order(colnames(X))] #OrderByHeader
headSel2=select(OBH, contains('OrfRep'), matches('Gene')) headSel <- select(OBH, contains("OrfRep"), matches("Gene"),
headSel2=select(headSel2, -'Gene.1',-'Gene.2') contains("Z_lm_K"), contains("Z_Shift_K"), contains("Z_lm_L"), contains("Z_Shift_L"))
headSel <- select(headSel, -"Gene.1", -"Gene.2", -"Gene.3")
}else if(length(inputFiles)==4){ headSel2 <- select(OBH, contains("OrfRep"), matches("Gene"))
X1 <- read.csv(file=inputFiles[1],stringsAsFactors=FALSE) #exp1File,stringsAsFactors=FALSE) headSel2 <- select(headSel2, -"Gene.1", -"Gene.2", -"Gene.3")
X2 <- read.csv(file=inputFiles[2],stringsAsFactors=FALSE) #exp2File,stringsAsFactors=FALSE)
X3 <- read.csv(file=inputFiles[3],stringsAsFactors=FALSE) #exp3File,stringsAsFactors=FALSE)
X4 <- read.csv(file=inputFiles[4],stringsAsFactors=FALSE) #exp4File,stringsAsFactors=FALSE)
X <- join(X1,X2,by="OrfRep")
X <- join(X,X3,by="OrfRep")
X <- join(X,X4,by="OrfRep")
OBH=X[,order(colnames(X))] #OrderByHeader
headSel=select(OBH, contains('OrfRep'), matches('Gene'), contains('Z_lm_K'), contains('Z_Shift_K'),contains('Z_lm_L'), contains('Z_Shift_L'))
headSel=select(headSel, -'Gene.1',-'Gene.2',-'Gene.3')
headSel2=select(OBH, contains('OrfRep'), matches('Gene'))
headSel2=select(headSel2, -'Gene.1',-'Gene.2',-'Gene.3')
} }
#headSel$contains('Z_Shift') %>% replace_na(0.001) # headSel$contains("Z_Shift") %>% replace_na(0.001)
headers<-colnames(headSel) headers <- colnames(headSel)
i=0 i <- 0
for(i in 1:length(headers)){ for (i in 1:length(headers)) {
if (grepl("Shift", headers[i])) {
if(grepl("Shift",headers[i])) { headSel[headers[i]][is.na(headSel[headers[i]])] <- 0.001
headSel[headers[i]][is.na(headSel[headers[i]])]=0.001
} }
if(grepl("Z_lm_",headers[i])) { if (grepl("Z_lm_", headers[i])) {
headSel[headers[i]][is.na(headSel[headers[i]])]=0.0001 headSel[headers[i]][is.na(headSel[headers[i]])] <- 0.0001
} }
} }
#2SD option code to exclude Z_lm values less than 2 standard Deviations # 2SD option code to exclude Z_lm values less than 2 standard Deviations
REMcRdy <- select(headSel, contains("OrfRep"), matches("Gene"), contains("Z_lm_"))
REMcRdy=select(headSel, contains('OrfRep'), matches('Gene'), contains('Z_lm_')) shiftOnly <- select(headSel, contains("OrfRep"), matches("Gene"), contains("Z_Shift"))
shiftOnly=select(headSel, contains('OrfRep'), matches('Gene'), contains('Z_Shift'))
# Code to replace the numeric (.1 .2 .3) headers with experiment names from StudyInfo.txt # Code to replace the numeric (.1 .2 .3) headers with experiment names from StudyInfo.txt
Labels <- read.csv(file="../Code/StudyInfo.csv",stringsAsFactors=FALSE,sep=",") Labels <- read.csv(file = "../Code/StudyInfo.csv", stringsAsFactors = FALSE, sep = ",")
# Using Text search grepl to relabel headers # Using Text search grepl to relabel headers
REMcRdyHdr=colnames(REMcRdy) REMcRdyHdr <- colnames(REMcRdy)
REMcRdyLabels='asdf' REMcRdyLabels <- "asdf"
shftHdr=colnames(shiftOnly) shftHdr <- colnames(shiftOnly)
shiftLabels='asdf' shiftLabels <- "asdf"
shiftLabels[1:2]<-shftHdr[1:2] shiftLabels[1:2] <- shftHdr[1:2]
REMcRdyLabels[1:2]<-REMcRdyHdr[1:2] REMcRdyLabels[1:2] <- REMcRdyHdr[1:2]
for(i in 3:(length(shftHdr))){ for (i in 3:(length(shftHdr))) {
if(i==3){ if (i == 3) {
shiftLabels[3]<-paste0(Labels[1,2],".",shftHdr[3]) shiftLabels[3] <- paste0(Labels[1, 2], ".", shftHdr[3])
REMcRdyLabels[3]<-paste0(Labels[1,2],".",REMcRdyHdr[3]) } REMcRdyLabels[3] <- paste0(Labels[1, 2], ".", REMcRdyHdr[3])
if(i==5){
shiftLabels[5]<-paste0(Labels[1,2],".",shftHdr[5])
REMcRdyLabels[5]<-paste0(Labels[1,2],".",REMcRdyHdr[5])
} }
if(i==7){ if (i == 5) {
shiftLabels[7]<-paste0(Labels[1,2],".",shftHdr[7]) shiftLabels[5] <- paste0(Labels[1, 2], ".", shftHdr[5])
REMcRdyLabels[7]<-paste0(Labels[1,2],".",REMcRdyHdr[7]) REMcRdyLabels[5] <- paste0(Labels[1, 2], ".", REMcRdyHdr[5])
}
if (i == 7) {
shiftLabels[7] <- paste0(Labels[1, 2], ".", shftHdr[7])
REMcRdyLabels[7] <- paste0(Labels[1, 2], ".", REMcRdyHdr[7])
}
if (grepl(".1", shftHdr[i], fixed = true)) {
shiftLabels[i] <- paste0(Labels[2, 2], ".", shftHdr[i])
REMcRdyLabels[i] <- paste0(Labels[2, 2], ".", REMcRdyHdr[i])
}
if (grepl(".2", shftHdr[i], fixed = true)) {
shiftLabels[i] < -paste0(Labels[3, 2], ".", shftHdr[i])
REMcRdyLabels[i] <- paste0(Labels[3, 2], ".", REMcRdyHdr[i])
}
if (grepl(".3", shftHdr[i], fixed = true)) {
shiftLabels[i] <- paste0(Labels[4, 2], ".", shftHdr[i])
REMcRdyLabels[i] <- paste0(Labels[4, 2], ".", REMcRdyHdr[i])
} }
if(grepl(".1",shftHdr[i],fixed=true)){
shiftLabels[i]<-paste0(Labels[2,2],".",shftHdr[i])
REMcRdyLabels[i]<-paste0(Labels[2,2],".",REMcRdyHdr[i])}
if (grepl(".2",shftHdr[i],fixed=true)){
shiftLabels[i]<-paste0(Labels[3,2],".",shftHdr[i])
REMcRdyLabels[i]<-paste0(Labels[3,2],".",REMcRdyHdr[i])}
if(grepl(".3",shftHdr[i],fixed=true)){
shiftLabels[i]<-paste0(Labels[4,2],".",shftHdr[i])
REMcRdyLabels[i]<-paste0(Labels[4,2],".",REMcRdyHdr[i])}
} }
for(i in 3:(length(REMcRdyLabels))){ for (i in 3:(length(REMcRdyLabels))) {
j=as.integer(i) j <- as.integer(i)
REMcRdyLabels[j]<- gsub("[.]", "_", REMcRdyLabels[j]) REMcRdyLabels[j] <- gsub("[.]", "_", REMcRdyLabels[j])
shiftLabels[j]<- gsub("[.]", "_", shiftLabels[j]) shiftLabels[j] <- gsub("[.]", "_", shiftLabels[j])
} }
colnames(shiftOnly)<- shiftLabels colnames(shiftOnly) <- shiftLabels
colnames(REMcRdy)<- REMcRdyLabels colnames(REMcRdy) <- REMcRdyLabels
combI=headSel2 #Starting Template orf, Genename columns combI <- headSel2 # starting Template orf, Genename columns
# headersRemc<-colnames(REMcRdy) # headersRemc<-colnames(REMcRdy)
# Reorder columns to produce an interleaved set of Z_lm and Shift data for all the cpps. # Reorder columns to produce an interleaved set of Z_lm and Shift data for all the cpps.
for(i in 3:length(colnames(REMcRdy))){ for (i in 3:length(colnames(REMcRdy))) {
combI=cbind.data.frame(combI, shiftOnly[i]) combI <- cbind.data.frame(combI, shiftOnly[i])
combI=cbind.data.frame(combI, REMcRdy[i]) combI <- cbind.data.frame(combI, REMcRdy[i])
} }
Vec1=NA Vec1 <- NA
Vec2=NA Vec2 <- NA
Vec3=NA Vec3 <- NA
Vec4=NA Vec4 <- NA
Vec5=NA Vec5 <- NA
Vec6=NA Vec6 <- NA
Vec7=NA Vec7 <- NA
Vec8=NA Vec8 <- NA
if(length(REMcRdy)==6){ if (length(REMcRdy) == 6) {
Vec1=abs(REMcRdy[,3])>=std Vec1 <- abs(REMcRdy[, 3]) >= std
Vec2=abs(REMcRdy[,4])>=std Vec2 <- abs(REMcRdy[, 4]) >= std
Vec3=abs(REMcRdy[,5])>=std Vec3 <- abs(REMcRdy[, 5]) >= std
Vec4=abs(REMcRdy[,6])>=std Vec4 <- abs(REMcRdy[, 6]) >= std
bolVec=Vec1 | Vec2 |Vec3 |Vec4 bolVec <- Vec1 | Vec2 | Vec3 | Vec4
REMcRdyGT2=REMcRdy[bolVec,1:2] REMcRdyGT2 <- REMcRdy[bolVec, 1:2]
REMcRdyGT2[ ,3:6]=REMcRdy[bolVec,3:6] REMcRdyGT2[, 3:6] <- REMcRdy[bolVec, 3:6]
shiftOnlyGT2=shiftOnly[bolVec,1:2] shiftOnlyGT2 <- shiftOnly[bolVec, 1:2]
shiftOnlyGT2[ ,3:6]=shiftOnly[bolVec,3:6] shiftOnlyGT2[, 3:6] <- shiftOnly[bolVec, 3:6]
} }
if(length(REMcRdy)==8){ if (length(REMcRdy) == 8) {
Vec1=abs(REMcRdy[,3])>=std Vec1 <- abs(REMcRdy[, 3]) >= std
Vec2=abs(REMcRdy[,4])>=std Vec2 <- abs(REMcRdy[, 4]) >= std
Vec3=abs(REMcRdy[,5])>=std Vec3 <- abs(REMcRdy[, 5]) >= std
Vec4=abs(REMcRdy[,6])>=std Vec4 <- abs(REMcRdy[, 6]) >= std
Vec5=abs(REMcRdy[,7])>=std Vec5 <- abs(REMcRdy[, 7]) >= std
Vec6=abs(REMcRdy[,8])>=std Vec6 <- abs(REMcRdy[, 8]) >= std
bolVec=Vec1 | Vec2 |Vec3 | Vec4 |Vec5 |Vec6 bolVec <- Vec1 | Vec2 | Vec3 | Vec4 | Vec5 | Vec6
REMcRdyGT2=REMcRdy[bolVec,1:2] REMcRdyGT2 <- REMcRdy[bolVec, 1:2]
REMcRdyGT2[ ,3:8]=REMcRdy[bolVec,3:8] REMcRdyGT2[, 3:8] <- REMcRdy[bolVec, 3:8]
shiftOnlyGT2=shiftOnly[bolVec,1:2] shiftOnlyGT2 <- shiftOnly[bolVec, 1:2]
shiftOnlyGT2[ ,3:8]=shiftOnly[bolVec,3:8] shiftOnlyGT2[, 3:8] <- shiftOnly[bolVec, 3:8]
} }
if(length(REMcRdy)==10){ if (length(REMcRdy) == 10) {
Vec1=abs(REMcRdy[,3])>=std Vec1 <- abs(REMcRdy[, 3]) >= std
Vec2=abs(REMcRdy[,4])>=std Vec2 <- abs(REMcRdy[, 4]) >= std
Vec3=abs(REMcRdy[,5])>=std Vec3 <- abs(REMcRdy[, 5]) >= std
Vec4=abs(REMcRdy[,6])>=std Vec4 <- abs(REMcRdy[, 6]) >= std
Vec5=abs(REMcRdy[,7])>=std Vec5 <- abs(REMcRdy[, 7]) >= std
Vec6=abs(REMcRdy[,8])>=std Vec6 <- abs(REMcRdy[, 8]) >= std
Vec7=abs(REMcRdy[,9])>=std Vec7 <- abs(REMcRdy[, 9]) >= std
Vec8=abs(REMcRdy[,10])>=std Vec8 <- abs(REMcRdy[, 10]) >= std
bolVec=Vec1 | Vec2 |Vec3 |Vec4|Vec5|Vec6|Vec7|Vec8 bolVec <- Vec1 | Vec2 | Vec3 | Vec4 | Vec5 | Vec6 | Vec7 | Vec8
REMcRdyGT2=REMcRdy[bolVec,1:2] REMcRdyGT2 <- REMcRdy[bolVec, 1:2]
REMcRdyGT2[ ,3:10]=REMcRdy[bolVec,3:10] REMcRdyGT2[, 3:10] <- REMcRdy[bolVec, 3:10]
shiftOnlyGT2=shiftOnly[bolVec,1:2] shiftOnlyGT2 <- shiftOnly[bolVec, 1:2]
shiftOnlyGT2[ ,3:10]=shiftOnly[bolVec,3:10] shiftOnlyGT2[, 3:10] <- shiftOnly[bolVec, 3:10]
} }
if(std!=0){ if (std != 0) {
REMcRdy=REMcRdyGT2 # [,2:length(REMcRdyGT2)] REMcRdy <- REMcRdyGT2 # [,2:length(REMcRdyGT2)]
shiftOnly=shiftOnlyGT2 # [,2:length(shiftOnlyGT2)] shiftOnly <- shiftOnlyGT2 # [,2:length(shiftOnlyGT2)]
} }
if(std==0){ if (std == 0) {
REMcRdy=REMcRdy # [,2:length(REMcRdy)] REMcRdy <- REMcRdy # [,2:length(REMcRdy)]
shiftOnly=shiftOnly # [,2:length(shiftOnly)] shiftOnly <- shiftOnly # [,2:length(shiftOnly)]
} }
# R places hidden "" around the header names. The following # R places hidden "" around the header names. The following
# is intended to remove those quote so that the "" do not blow up the Java REMc. # is intended to remove those quote so that the "" do not blow up the Java REMc.
# Use ,quote=F in the write.csv statement to fix R output file. # Use ,quote=F in the write.csv statement to fix R output file.
#write.csv(combI,file=file.path(outDir,"CombinedKLzscores.csv"),row.names=FALSE) # write.csv(combI,file.path(outDir,"CombinedKLzscores.csv"), row.names = FALSE)
write.csv(REMcRdy,file=file.path(outDir,"REMcRdy_lm_only.csv"),row.names=FALSE, quote=F) write.csv(REMcRdy, file.path(outDir, "REMcRdy_lm_only.csv"), row.names = FALSE, quote = FALSE)
write.csv(shiftOnly,file=file.path(outDir,"Shift_only.csv"),row.names=FALSE, quote=F) write.csv(shiftOnly, file.path(outDir, "Shift_only.csv"), row.names = FALSE, quote = FALSE)
#LabelStd <- read.table(file="./parameters.csv",stringsAsFactors=FALSE,sep=",") #LabelStd <- read.table(file="./parameters.csv",stringsAsFactors = FALSE,sep = ",")
LabelStd<-read.csv(file=studyInfo,stringsAsFactors=FALSE) LabelStd <- read.csv(file = studyInfo, stringsAsFactors = FALSE)
print(std) print(std)
LabelStd[,4]=as.numeric(std) LabelStd[, 4] <- as.numeric(std)
write.csv(LabelStd,file=file.path(outDir,"parameters.csv"),row.names=FALSE) write.csv(LabelStd, file = file.path(outDir, "parameters.csv"), row.names = FALSE)
write.csv(LabelStd,file=studyInfo,row.names=FALSE) write.csv(LabelStd, file = studyInfo, row.names = FALSE)

37
workflow/qhtcp-workflow
View File

@@ -1620,7 +1620,12 @@ gta() {
for combo in "${study_combos[@]}"; do for combo in "${study_combos[@]}"; do
# Split on comma and assign to array # Split on comma and assign to array
IFS=',' read -ra studies <<< "$combo" IFS=',' read -ra studies <<< "$combo"
r_gta_pairwiselk "${studies[0]}" "${studies[1]}" "$STUDY_INFO_FILE" "$gta_out_dir" r_gta_pairwiselk \
"${studies[0]}" \
"${studies[1]}" \
"$STUDY_INFO_FILE" \
"Average_GOTerms_All.csv" \
"$gta_out_dir"
done done
# All studies # All studies
@@ -1663,14 +1668,15 @@ wrapper r_gta
# * Is GTAtemplate.R actually a template? # * Is GTAtemplate.R actually a template?
# * Do we need to allow user customization? # * Do we need to allow user customization?
# #
# Files # INPUT
# #
# * [gene_association.sgd](https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd) # * [gene_association.sgd](https://downloads.yeastgenome.org/curation/chromosomal_feature/gene_association.sgd)
# * go_terms.tab # * go_terms.tab
# #
# Output # OUTPUT
#
# * Average_GOTerms_All.csv
# #
# *
# #
# @arg $1 string Exp# name # @arg $1 string Exp# name
# @arg $2 string ZScores_Interaction.csv file # @arg $2 string ZScores_Interaction.csv file
@@ -1683,7 +1689,7 @@ r_gta() {
cat <<-EOF cat <<-EOF
EOF EOF
script="$APPS_DIR/r/GTAtemplate.R" script="$APPS_DIR/r/gtaTemplate.R"
[[ -d $5 ]] || mkdir -p "$5" [[ -d $5 ]] || mkdir -p "$5"
debug "$RSCRIPT $script $*" debug "$RSCRIPT $script $*"
"$RSCRIPT" "$script" \ "$RSCRIPT" "$script" \
@@ -1701,11 +1707,13 @@ wrapper r_gta_pairwiselk
# #
# TODO # TODO
# #
# * Should move directory creation from PairwiseLK.R to gta module # * Move directory creation from PairwiseLK.R to gta module
# * Needs better output filenames and directory organization
# * Needs more for looping to reduce verbosity
# #
# Files # INPUT
# #
# * # * Average_GOTerms_All.csv
# * # *
# #
# Output # Output
@@ -1724,7 +1732,7 @@ wrapper r_gta_pairwiselk
# @arg $4 string output directory # @arg $4 string output directory
# #
r_gta_pairwiselk() { r_gta_pairwiselk() {
debug "Running: ${FUNCNAME[0]}" debug "Running: ${FUNCNAME[0]} $*"
cat <<-EOF cat <<-EOF
EOF EOF
@@ -1734,7 +1742,12 @@ r_gta_pairwiselk() {
[[ -d $4 ]] || mkdir -p "$4" [[ -d $4 ]] || mkdir -p "$4"
debug "$RSCRIPT $script $*" debug "$RSCRIPT $script $*"
"$RSCRIPT" "$script" "$@" "$RSCRIPT" "$script" \
"$1" \
"$2" \
"$3" \
"$4" \
"${@:5}"
} }
@@ -1842,6 +1855,10 @@ r_interactions() {
wrapper r_join_interactions wrapper r_join_interactions
# @description JoinInteractExps3dev.R creates REMcRdy_lm_only.csv and Shift_only.csv # @description JoinInteractExps3dev.R creates REMcRdy_lm_only.csv and Shift_only.csv
# #
# TODO
# * Needs more loops to reduce verbosity
#
#
# Output # Output
# #
# * REMcRdy_lm_only.csv # * REMcRdy_lm_only.csv