GSE75214 Processing Pipeline

Publication

Gut (2022) — PMID 34853057

Dataset

Mucosal gene expression profiling in patients with inflammatory bowel disease study

1. 1

   Probe level analysis was performed on the Affymetrix raw data (.cel files) with the robust multichip average (RMA) method implemented in the Bioconductor package 'aroma.affymetrix' to obtain a log2 expression value for each gene probe set

   R vR 4.3.x (Bioconductor 3.18)

   $ Bash example

   #!/bin/bash
   
   # Define environment variables for input/output
   # Replace with your actual CEL file directory and desired output file
   export CEL_FILES_DIR="data/cel_files"
   export OUTPUT_FILE="results/rma_log2_expression.tsv"
   
   # Create output directory if it doesn't exist
   mkdir -p $(dirname "$OUTPUT_FILE")
   
   # --- R Installation (commented out) ---
   # # Install R if not available (example for Ubuntu/Debian)
   # # sudo apt update
   # # sudo apt install r-base
   
   # # Install Bioconductor and aroma.affymetrix package
   # # Start R and run:
   # # if (!requireNamespace("BiocManager", quietly = TRUE))
   # #     install.packages("BiocManager")
   # # BiocManager::install("aroma.affymetrix") # aroma.affymetrix is part of aroma.core
   
   # --- R Script for RMA Analysis ---
   # Create a temporary R script
   cat << 'EOF' > rma_analysis.R
   # R script (rma_analysis.R)
   
   library(aroma.affymetrix)
   
   # --- Configuration ---
   cel_files_dir <- Sys.getenv("CEL_FILES_DIR", "path/to/your/cel_files") # Directory containing .cel files
   output_file <- Sys.getenv("OUTPUT_FILE", "rma_log2_expression.tsv") # Output file name
   
   # Check if the directory exists
   if (!dir.exists(cel_files_dir)) {
       stop(paste("CEL files directory not found:", cel_files_dir))
   }
   
   # --- Load Affymetrix CEL files ---
   # Create an AffymetrixCelSet object from the specified directory
   # This will automatically detect the chip type from the .cel files
   message(paste("Loading CEL files from:", cel_files_dir))
   cs <- AffymetrixCelSet$byPath(cel_files_dir)
   message(paste("Detected chip type:", getChipType(cs)))
   message(paste("Number of arrays:", length(cs)))
   
   # --- Perform Robust Multichip Average (RMA) ---
   # This function performs background correction, quantile normalization,
   # and median polish summarization to obtain log2 expression values.
   message("Performing RMA...")
   ds <- doRMA(cs, verbose=TRUE)
   
   # --- Extract log2 expression matrix ---
   # The expression values are already in log2 scale after RMA
   message("Extracting log2 expression matrix...")
   expr_matrix <- extractMatrix(ds)
   
   # --- Write results to file ---
   message(paste("Writing log2 expression matrix to:", output_file))
   write.table(expr_matrix, file=output_file, sep="\t", quote=FALSE, row.names=TRUE)
   
   message("RMA analysis complete.")
   EOF
   
   # Execute the R script
   Rscript rma_analysis.R
   
   # Clean up the temporary R script
   rm rma_analysis.R

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