GSE37892 Processing Pipeline

Publication

Life science alliance (2020) — PMID 32817263

Dataset

A seven-gene signature aggregates a subgroup of stage II colon cancers with stage III.

1. 1

   CEL files were processed in the R (v.

   R vUnknown GitHub

   $ Bash example

   # Install R (if not already installed)
   # conda install -c conda-forge r-base
   
   # Install Bioconductor packages for CEL file processing (e.g., 'affy' for RMA normalization)
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")'
   # R -e 'BiocManager::install("affy")'
   
   # Create a placeholder R script for processing CEL files
   cat << 'EOF' > process_cel_files.R
   # Load necessary libraries
   library(affy)
   
   # Define the directory containing CEL files
   # Replace "." with the actual path to your CEL files if they are not in the current directory
   cel_dir <- "."
   
   # List all CEL files in the specified directory
   cel_files <- list.celfiles(path = cel_dir, full.names = TRUE)
   
   # Check if any CEL files were found
   if (length(cel_files) == 0) {
     stop("No CEL files found in the specified directory: ", cel_dir)
   }
   
   message(paste("Found", length(cel_files), "CEL files."))
   
   # Read CEL files into an AffyBatch object
   # This step can be memory intensive depending on the number and size of CEL files
   raw_data <- ReadAffy(filenames = cel_files)
   
   # Perform Robust Multi-array Average (RMA) normalization
   # RMA is a common method for background correction, normalization, and summarization of Affymetrix data
   normalized_data <- rma(raw_data)
   
   # Extract the expression matrix (log2 transformed and normalized intensities)
   expression_matrix <- exprs(normalized_data)
   
   # Save the processed expression matrix to a CSV file
   output_csv_file <- "processed_cel_expression.csv"
   write.csv(expression_matrix, file = output_csv_file, row.names = TRUE)
   message(paste("Processed expression matrix saved to:", output_csv_file))
   
   # Optionally, save the entire ExpressionSet object for further analysis in R
   output_rdata_file <- "processed_cel_eset.RData"
   save(normalized_data, file = output_rdata_file)
   message(paste("Normalized ExpressionSet object saved to:", output_rdata_file))
   EOF
   
   # Execute the R script to process CEL files
   # Ensure that your CEL files are in the directory specified by 'cel_dir' in the R script
   Rscript process_cel_files.R

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2. 2

   2.10.0)/Bioconductor (v 2.5) environment.

   R v2.5 GitHub

   $ Bash example

   # This step describes the R/Bioconductor environment used, not a specific execution command.
   # The description indicates that the analysis was performed within an R (v 2.10.0, though the prompt specifies 2.5) and Bioconductor (v 2.5) environment.
   # No specific R script or command is provided in the description.
   
   # To use R version 2.5 with Bioconductor 2.5, you would typically need to have it installed.
   # Installation of such old R/Bioconductor versions can be complex and might require specific system configurations or virtual environments.
   # For modern systems, using tools like `conda` or `renv` for environment management is recommended, but finding R 2.5 and Bioconductor 2.5 via conda might be challenging due to their age.
   
   # Example of how one might launch R, assuming it's in the PATH and the correct version is active:
   # R --version # To check the R version
   # Rscript -e "packageVersion('Biobase')" # To check a core Bioconductor package version, indicating Bioconductor environment
   
   # If a specific R script were provided, the command would typically look like:
   # Rscript your_analysis_script.R arg1 arg2
   # Or for interactive use:
   # R

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3. 3

   Pre-processing steps (background adjustment, normalization and summarization) were performed with the GCRMA package (v.2.18.1)

   GCRMA v2.18.1 GitHub

   $ Bash example

   # Install R and Bioconductor packages if not already installed (uncomment and run if needed)
   # R -e "install.packages('BiocManager')"
   # R -e "BiocManager::install('gcrma')"
   # R -e "BiocManager::install('affy')"
   # R -e "BiocManager::install('hgu133plus2.db')" # Example: Replace with the appropriate chip annotation package for your data (e.g., hgu133plus2.db, hgu95av2.db, etc.)
   
   # Create a dummy R script to perform GCRMA pre-processing
   cat << 'EOF' > run_gcrma_preprocessing.R
   # Load necessary libraries
   library(affy)
   library(gcrma)
   
   # --- Configuration --- #
   # Define the directory containing your raw Affymetrix .CEL files
   cel_files_directory <- "./path/to/your/cel_files"
   
   # Define the output file name for the normalized expression matrix
   output_expression_file <- "gcrma_normalized_expression.txt"
   # --- End Configuration --- #
   
   # Check if the CEL files directory exists
   if (!dir.exists(cel_files_directory)) {
       stop(paste("Error: CEL files directory not found at", cel_files_directory))
   }
   
   # List all .CEL files in the specified directory
   cel_files <- list.files(path = cel_files_directory, pattern = "\\.CEL$", full.names = TRUE, ignore.case = TRUE)
   
   if (length(cel_files) == 0) {
       stop(paste("No .CEL files found in", cel_files_directory, ". Please ensure files are present and have a .CEL extension."))
   }
   
   message(paste("Found", length(cel_files), ".CEL files."))
   
   # Read CEL files into an AffyBatch object
   # This step requires that all CEL files are from the same chip type
   # and that the corresponding chip annotation package is installed.
   raw_data <- ReadAffy(filenames = cel_files)
   
   message("Performing GCRMA pre-processing (background adjustment, normalization, summarization)...")
   
   # Perform GCRMA pre-processing
   # The gcrma function performs background adjustment, normalization, and summarization
   # by default, as described in the pipeline step.
   eset <- gcrma(raw_data)
   
   # Extract the normalized expression matrix
   expression_matrix <- exprs(eset)
   
   # Write the normalized expression matrix to a tab-separated file
   write.table(expression_matrix, file = output_expression_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message(paste("GCRMA pre-processing complete. Normalized expression matrix saved to:", output_expression_file))
   EOF
   
   # Execute the R script
   Rscript run_gcrma_preprocessing.R

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Tools Used