GSE28359 Processing Pipeline

Publication

Nature (2016) — PMID 27121842

Dataset

Aryl hydrocarbon receptor antagonists promote the expansion of human hematopoietic stem cells

1. 1

   gcRMA

   gcrma (R package) (Inferred with models/gemini-2.5-flash) vN/A GitHub

   $ Bash example

   # Install R and Bioconductor if not already installed.
   # For example, using conda:
   # conda create -n r_env r-base bioconductor-affy bioconductor-gcrma -y
   # conda activate r_env
   
   # Create an R script to perform gcRMA normalization
   cat << 'EOF' > run_gcrma.R
   # Load necessary libraries
   library(affy)
   library(gcrma)
   
   # Define input directory containing .CEL files
   # Replace 'path/to/your/cel_files' with the actual path to your raw microarray data.
   cel_files_dir <- "path/to/your/cel_files"
   
   # Read .CEL files from the specified directory.
   # This assumes all .CEL files in the directory are part of the same experiment
   # and should be normalized together.
   cel_files <- list.files(cel_files_dir, pattern = "\\.CEL$", full.names = TRUE)
   if (length(cel_files) == 0) {
     stop(paste("No .CEL files found in the specified directory:", cel_files_dir))
   }
   raw_data <- ReadAffy(filenames = cel_files)
   
   # Perform gcRMA normalization.
   # This step adjusts for background noise and normalizes probe intensities.
   normalized_data <- gcrma(raw_data)
   
   # Extract the normalized expression matrix.
   expression_matrix <- exprs(normalized_data)
   
   # Define output file path.
   # Replace 'normalized_gcrma_expression.tsv' with your desired output file name and path.
   output_file <- "normalized_gcrma_expression.tsv"
   
   # Save the normalized expression matrix to a TSV file.
   # row.names = TRUE ensures probe IDs are included.
   write.table(expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message(paste("gcRMA normalization complete. Normalized data saved to:", output_file))
   EOF
   
   # Execute the R script to perform gcRMA normalization.
   Rscript run_gcrma.R
   
   # Example usage:
   # 1. Place your .CEL files into a directory, e.g., 'raw_cel_data'.
   # 2. Edit 'run_gcrma.R' to set 'cel_files_dir' to 'raw_cel_data' and 'output_file' to your desired output path.
   #    e.g., cel_files_dir <- "raw_cel_data"
   #    e.g., output_file <- "results/normalized_gcrma_expression.tsv"
   # 3. Ensure the output directory exists: mkdir -p results
   # 4. Run the script: Rscript run_gcrma.R

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