GSE27901 Processing Pipeline

Publication

Molecular cell (2020) — PMID 33157015

Dataset

Transactivation-deficient p53 Mutants in Ras-induced Cellular Senescence

1. 1

   BRB-ArrayTools v3.8.1 & RMA

   BRB-ArrayTools v3.8.1 GitHub

   $ Bash example

   # BRB-ArrayTools is primarily a Windows-based graphical user interface (GUI) software developed by the NCI.
   # The Robust Multi-array Average (RMA) normalization is typically performed interactively within the BRB-ArrayTools GUI.
   # For programmatic execution of the RMA algorithm, the 'affy' package in R is commonly used, which implements the same statistical method.
   
   # Install R and the 'affy' Bioconductor package if not already present
   # conda install -c r r-base
   # R -e "install.packages('BiocManager', repos='https://cloud.r-project.org')"
   # R -e "BiocManager::install('affy')"
   
   # Create a placeholder directory for raw .CEL files
   mkdir -p raw_cel_files
   # Placeholder: Copy your actual .CEL files into the 'raw_cel_files' directory.
   # Example: cp /path/to/your/sample1.CEL raw_cel_files/
   # Example: cp /path/to/your/sample2.CEL raw_cel_files/
   
   # Create an R script to perform RMA normalization using the 'affy' package
   cat << 'EOF' > run_rma.R
   library(affy)
   
   # Define the directory containing .CEL files
   cel_dir <- "./raw_cel_files" # Placeholder: Replace with actual path to .CEL files if different
   
   # Get list of .CEL files
   cel_files <- list.files(cel_dir, pattern = "\\.CEL$", full.names = TRUE, ignore.case = TRUE)
   
   if (length(cel_files) == 0) {
     stop("No .CEL files found in the specified directory: ", cel_dir, "\nPlease ensure raw .CEL files are present.")
   }
   
   message("Found ", length(cel_files), " .CEL files for RMA normalization.")
   
   # Read .CEL files into an AffyBatch object
   # For more complex experiments, a phenoData file (e.g., a tab-separated file describing samples)
   # can be loaded and passed to ReadAffy. For basic RMA, ReadAffy can often infer from filenames.
   affy_batch <- ReadAffy(filenames = cel_files)
   
   # Perform RMA normalization
   message("Performing RMA normalization...")
   rma_data <- rma(affy_batch)
   
   # Extract normalized expression matrix
   normalized_expression_matrix <- exprs(rma_data)
   
   # Define output file path
   output_file <- "normalized_expression_rma.tsv"
   
   # Write normalized expression matrix to a TSV file
   write.table(normalized_expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message("RMA normalization complete. Normalized expression saved to: ", output_file)
   EOF
   
   # Execute the R script for RMA normalization
   Rscript run_rma.R

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