Restructure cwl

workflows/kallisto_collect/kallisto_collect.R

@@ -13,46 +13,59 @@ library("jsonlite")
 
 args <- commandArgs(trailingOnly = T)
 
-in_kallisto_results <- args[1]
-in_metadata_file <- args[2]
-in_metadata_sample <- args[3]
-in_metadata_factor <- args[4]
-out_folder <- args[5]
+inKallistoResults <- args[1]
+inMetadataFile <- args[2]
+inMetadataSample <- args[3]
+inMetadataFactorList <- args[4]
+outFolder <- args[5]
+
+# inKallistoResults <- "../../runs/kallisto/kallisto_results"
+# inMetadataFile <- "../../runs/isaSampleToRawDataSeq/rnaseq-samples.csv"
+# inMetadataSample <- "Input [Source Name]"
+# inMetadataFactorList <- list("Factor [Photosynthesis mode]")
+# inMetadataDataCol <- "Output [Data]"
+# outFolder <- "."
 
 ################################################
 #### If it does not exist, create out dir
 ################################################
 
-dir.create(out_folder, recursive = T, showWarnings = F)
+dir.create(outFolder, recursive = T, showWarnings = F)
 
 ################################################
 #### Read ISA sample metadata
 ################################################
 
-samples <- read.table(file = in_metadata_file, sep = "\t", header = TRUE)
+samples <- read.csv(file = inMetadataFile, check.names = FALSE)
 
 ################################################
 #### Read Kallisto results
 ################################################
 
-base_dir <- in_kallisto_results
+base_dir <- inKallistoResults
 
 # A list of paths to the kallisto results indexed by the sample IDs is collated with
 kal_dirs <- dir(base_dir, full.names = T) ## Sleuth requires full paths
 
-s2c <- samples[order(samples[[in_metadata_sample]]), c(in_metadata_sample, in_metadata_factor, "Data.File.Name")]
+s2c <- samples[order(samples[[inMetadataSample]]), c(inMetadataSample, unlist(inMetadataFactorList), inMetadataDataCol)]
 
 # For kallisto / sleuth: 's2c' (sample_to_covariates) must contain a column named 'sample'
-colnames(s2c)[1:2] <- c("sample", "condition")
-
-s2c$out_name <- gsub(".fastq.+", "", s2c$Data.File.Name)
-path_df <- data.frame(path = kal_dirs, out_name = gsub(".+/", "", kal_dirs))
-s2c <- merge(s2c, path_df, by = "out_name")
+colnames(s2c)[1] <- c("sample")
+path_df <- data.frame(path = kal_dirs, sample = basename(kal_dirs))
+s2c <- merge(s2c, path_df, by = "sample")
 
 # Build a sleuth object
-so <- sleuth_prep(s2c, full_model = ~condition, num_cores = 1)
 
-save(so, file = paste(out_folder, "kallisto_sleuthObject.RData", sep = "/"))
+factors <- sapply(inMetadataFactorList, function(x) x[[1]])
+## Annoying workaround to prevent formula error with special chars in column headers
+colnames(s2c) <- make.names(colnames(s2c))
+factors <- make.names(factors)
+
+design_formula <- as.formula(paste("~", paste(rev(factors), collapse = " + ")))
+
+so <- sleuth_prep(s2c, full_model = design_formula)
+
+save(so, file = file.path(outFolder, "kallisto_sleuthObject.RData"))
 
 ################################################
 #### Extract expression tables
@@ -62,14 +75,13 @@ save(so, file = paste(out_folder, "kallisto_sleuthObject.RData", sep = "/"))
 expression_data <- kallisto_table(so)
 
 ## write to file
-write.csv(expression_data, paste(out_folder, "/kallisto_df.csv", sep = "/"), row.names = F)
+write.csv(expression_data, paste(outFolder, "/kallisto_df.csv", sep = "/"), row.names = F)
 
 ## as tpm matrix (gene x sample)
-# Replacing pivot_wider() with base R operations
 tpm_table <- reshape(expression_data, idvar = "target_id", timevar = "sample", direction = "wide", v.names = "tpm")
 
 # Write to file
-write.csv(tpm_table, paste(out_folder, "/kallisto_tpmMatrix.csv", sep = "/"), row.names = F)
+write.csv(tpm_table, file.path(outFolder, "kallisto_tpmMatrix.csv"), row.names = F)
 
 ################################################
 #### Summarize mapping stats
@@ -82,4 +94,4 @@ for (i in dir(kal_dirs, pattern = ".json", full.names = T)) {
   mapping_stats <- rbind(mapping_stats, z)
 }
 
-write.csv(mapping_stats, paste(out_folder, "/kallisto_mappingStats.csv", sep = "/"), row.names = F)
+write.csv(mapping_stats, file.path(outFolder, "kallisto_mappingStats.csv"), row.names = F)