GSE35338 Processing Pipeline

GSE code_examples 1 step

Publication

Genomic analysis of the molecular neuropathology of tuberous sclerosis using a human stem cell model.

Genome medicine (2016) — PMID 27655340

Dataset

GSE35338

Expression data from reactive astrocytes acutely purified from young adult mouse brains

Warning: Pipeline descriptions and code snippets may be inferred or AI-generated. Use them only as a starting point to guide analysis, and validate before use.
  1. 1

    CEL files were analyzed wth the Arraystar 4.0 software (DNAstar) using RMA processing with quantile normalization.

    Arraystar v4.0
    $ Bash example
    # Arraystar 4.0 is a commercial, GUI-based software. The following R code provides a conceptual command-line equivalent for RMA processing with quantile normalization, commonly performed on CEL files, using the 'affy' package. This is a programmatic approximation of the described analysis step.
    
    # Install R if not already installed (e.g., on Ubuntu/Debian)
    # sudo apt-get update
    # sudo apt-get install r-base
    
    # Install Bioconductor and 'affy' package in R
    # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager"); BiocManager::install("affy")'
    
    # Create an R script to perform RMA and quantile normalization
    cat << 'EOF' > analyze_cel_files.R
    library(affy)
    
    # Define the directory containing CEL files
    cel_dir <- "." # Assuming CEL files are in the current directory
    # Or specify a path: cel_dir <- "/path/to/your/cel_files"
    
    # List all CEL files
    cel_files <- list.files(path = cel_dir, pattern = "\\.CEL$", full.names = TRUE, ignore.case = TRUE)
    
    if (length(cel_files) == 0) {
      stop("No CEL files found in the specified directory.")
    }
    
    # Read CEL files
    print(paste("Reading", length(cel_files), "CEL files..."))
    affy_batch <- ReadAffy(filenames = cel_files)
    
    # Perform RMA processing with quantile normalization
    print("Performing RMA processing with quantile normalization...")
    eset <- rma(affy_batch)
    
    # Extract expression matrix
    expression_matrix <- exprs(eset)
    
    # Save the normalized expression matrix to a CSV file
    output_file <- "normalized_expression_matrix.csv"
    write.csv(expression_matrix, file = output_file)
    
    print(paste("Analysis complete. Normalized expression matrix saved to", output_file))
    EOF
    
    # Execute the R script
    Rscript analyze_cel_files.R
Raw Source Text
CEL files were analyzed wth the Arraystar 4.0 software (DNAstar) using RMA processing with quantile normalization.
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