GSE31595 Processing Pipeline

Publication

Life science alliance (2020) — PMID 32817263

Dataset

Gene Expression Profiles in Stage II and III Colon Cancer. Application of a 128-gene signature

1. 1

   The processing and analysis of expression data were performed using the statistical software R and Bioconductor.

   R vNot specified GitHub

   $ Bash example

   # Install R and Bioconductor (example using Conda)
   # conda create -n r_env r-base bioconductor-biocmanager
   # conda activate r_env
   # R -e "BiocManager::install(c('limma', 'DESeq2', 'edgeR'))" # Example Bioconductor packages for expression analysis
   
   # Execute an R script for expression data processing and analysis
   # Replace 'expression_data.csv' with your actual input data file (e.g., counts, normalized expression)
   # Replace 'analysis_script.R' with the actual R script performing the analysis
   # Replace 'output_results.tsv' with the actual output file (e.g., differential expression results, processed data)
   Rscript analysis_script.R expression_data.csv output_results.tsv

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2. 2

   For all Affymetrix CEL files the background were corrected and the expression were normalized using robust multiarray average (RMA).

   Microarray v1.78.0 GitHub

   $ Bash example

   # Install R if not already installed
   # sudo apt-get update
   # sudo apt-get install r-base
   
   # Install Bioconductor and affy package
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")'
   # R -e 'BiocManager::install("affy")'
   
   # Create a dummy directory for CEL files (replace with your actual CEL file directory)
   mkdir -p cel_files
   
   # Create dummy CEL files for demonstration purposes.
   # In a real scenario, these would be your actual Affymetrix .CEL files.
   touch cel_files/sample1.CEL
   touch cel_files/sample2.CEL
   touch cel_files/sample3.CEL
   
   # Create an R script to perform RMA normalization
   cat << 'EOF' > run_rma.R
   library(affy)
   
   # Define the directory containing CEL files
   cel_files_dir <- "cel_files"
   
   # List all CEL files in the specified directory
   cel_files <- list.files(path = cel_files_dir, pattern = "\\.CEL$", full.names = TRUE, ignore.case = TRUE)
   
   if (length(cel_files) == 0) {
     stop(paste("Error: No CEL files found in", cel_files_dir, ". Please ensure your CEL files are in this directory."))
   }
   
   message(paste("Found", length(cel_files), "CEL files."))
   
   # Read CEL files into an AffyBatch object
   # For basic RMA, a simple ReadAffy call is sufficient.
   # For more complex experiments, a phenoData file might be needed.
   affy_batch <- ReadAffy(filenames = cel_files)
   
   message("Performing RMA normalization...")
   # Perform Robust Multiarray Average (RMA) normalization
   # This function performs background correction, normalization, and summarization.
   eset <- rma(affy_batch)
   
   # Extract the normalized expression matrix
   expression_matrix <- exprs(eset)
   
   # Define the output file name
   output_file <- "rma_normalized_expression.tsv"
   
   # Write the expression matrix to a tab-separated file
   # row.names = TRUE to keep probe IDs as the first column
   write.table(expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message(paste("RMA normalized expression matrix successfully written to", output_file))
   EOF
   
   # Execute the R script
   Rscript run_rma.R

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Tools Used