GSE66696 Processing Pipeline

Publication

PLoS genetics (2015) — PMID 25835743

Dataset

Whole brain RNA from congenic littermates does not support a general effect of Nxf1 CAST alleles on alternative pre-mRNA processing

1. 1

   Data processed using Affymetrix package (Affy Power Tools) apt-probeset-summarize.

   Microarray v2.12.0 (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Affymetrix Power Tools (APT) is typically downloaded as a binary package from Thermo Fisher Scientific.
   # Installation instructions vary by OS. For example, on Linux:
   # wget https://assets.thermofisher.com/TFS-Assets/LSG/software/APT_2.12.0_Linux_x64.zip
   # unzip APT_2.12.0_Linux_x64.zip
   # cd APT_2.12.0_Linux_x64
   # export PATH=$PWD/bin:$PATH
   
   # Placeholder for input CEL files, output directory, and array-specific reference files.
   # Replace 'sample*.CEL' with actual input CEL file names.
   # Replace 'HG-U133_Plus_2.cdf' and 'HG-U133_Plus_2' with the appropriate CDF file and chip type for your array.
   INPUT_CEL_FILES="sample1.CEL sample2.CEL sample3.CEL"
   OUTPUT_DIR="apt_summarize_output"
   PROBESET_FILE="HG-U133_Plus_2.cdf" # Example: CDF file for a common Affymetrix array
   CHIP_TYPE="HG-U133_Plus_2" # Example: Chip type corresponding to the array
   
   mkdir -p "${OUTPUT_DIR}"
   
   apt-probeset-summarize \
       -a rma \
       -o "${OUTPUT_DIR}" \
       -p "${PROBESET_FILE}" \
       -c "${CHIP_TYPE}" \
       ${INPUT_CEL_FILES}

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2. 2

   Iter-plier algorithm used to quantify probesets.

   Iter-plier vImplemented in `affy` R package (Bioconductor 3.18) (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install R and Bioconductor if not already present
   # R -e "install.packages('BiocManager')"
   # R -e "BiocManager::install('affy')"
   
   # Create an R script for Iter-plier quantification
   cat << 'EOF' > quantify_probesets_iterplier.R
   # Load the affy package, which implements the Iter-plier algorithm
   library(affy)
   
   # --- Configuration ---
   # Specify the path to your Affymetrix CEL files.
   # IMPORTANT: Replace "./" with the actual directory containing your CEL files.
   CEL_FILES_PATH <- "./" # Example: current directory
   
   # --- Data Loading ---
   # Read Affymetrix CEL files into an AffyBatch object.
   # This object contains the raw probe intensity data.
   # The 'affy' package automatically uses the appropriate CDF (Chip Description File)
   # based on the array type detected in the CEL files.
   message(paste("Loading CEL files from:", CEL_FILES_PATH))
   if (!dir.exists(CEL_FILES_PATH)) {
     stop(paste("Error: CEL files directory not found:", CEL_FILES_PATH))
   }
   raw_data <- ReadAffy(celfile.path = CEL_FILES_PATH)
   
   # --- Probeset Quantification using Iter-plier Algorithm ---
   # The 'expresso' function performs a complete quantification pipeline,
   # allowing specification of methods for background correction, normalization,
   # PM/MM correction, and summarization.
   # Here, we explicitly use "iterplier" for background correction.
   # Other common methods are quantile normalization and median polish summarization.
   message("Performing probeset quantification using Iter-plier background correction...")
   eset <- expresso(raw_data,
                    bgcorrect.method = "iterplier",
                    normalize.method = "quantiles",
                    pmcorrect.method = "pmonly", # Perfect Match only correction
                    summary.method = "medianpolish")
   
   # --- Extract and Save Results ---
   # Extract the expression matrix from the ExpressionSet object.
   expression_matrix <- exprs(eset)
   
   # Save the quantified probeset data to a CSV file.
   output_filename <- "probeset_quantification_iterplier.csv"
   write.csv(expression_matrix, output_filename, row.names = TRUE)
   
   message(paste("Probeset quantification complete. Output saved to:", output_filename))
   EOF
   
   # Execute the R script
   Rscript quantify_probesets_iterplier.R

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3. 3

   probe group file: GPL13185_mjay.pgf

   Affymetrix Power Tools (APT) (Inferred with models/gemini-2.5-flash) vLatest (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Installation of Affymetrix Power Tools (APT) from Thermo Fisher Scientific:
   # APT is typically downloaded as a binary package from the official website.
   # Example for Linux:
   # wget https://assets.thermofisher.com/TFS-Assets/LSG/software/APT_2.11.2_Linux_x86_64.zip
   # unzip APT_2.11.2_Linux_x86_64.zip
   # export PATH=$PATH:/path/to/apt/bin
   
   # The GPL13185_mjay.pgf file is a probe group file, which defines the probe sets
   # for the Affymetrix Mouse Gene 1.0 ST Array (GPL13185).
   # This file is a reference dataset used by Affymetrix Power Tools for data processing.
   # Ensure the probe group file is accessible in your working directory or specified path.
   # Example: Copying or linking the reference file if it's stored elsewhere.
   # cp /path/to/reference_files/GPL13185_mjay.pgf .
   
   # Example command: Using apt-probeset-summarize to process CEL files
   # and generate expression summaries using the specified probe group file.
   # Replace 'input_cel_files.txt' with a file containing a list of your CEL file paths (one per line).
   # Replace 'output_dir' with your desired output directory.
   # This command assumes you have raw Affymetrix .CEL files to process.
   apt-probeset-summarize --cel-files input_cel_files.txt --probe-group-file GPL13185_mjay.pgf --output-dir output_dir --log-file apt_summarize.log

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4. 4

   Data were analyzed with Omniviewer software: http://exon.ucsc.edu/omniviewer/.

   Omniviewer vN/A GitHub

   $ Bash example

   # Omniviewer is a web-based genome browser/viewer.
   # Data analysis with Omniviewer typically involves interactive exploration through its web interface.
   # There is no direct command-line execution for the 'analysis' step itself.
   # The following command is a placeholder to indicate the action of viewing data.
   
   echo "Data were analyzed interactively using the Omniviewer web interface at http://exon.ucsc.edu/omniviewer/."

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5. 5

   The Omniviewer output is available on the series record.

   OmniViewer vNot specified

   $ Bash example

   # This step describes the availability of Omniviewer output on a series record.
   # OmniViewer is primarily a web-based interactive visualization tool for multi-omics data.
   # The "output" likely refers to data viewable through the OmniViewer web interface
   # or a report/export generated by it.
   #
   # As there is no direct command-line execution implied for *generating* this output
   # by a web-based viewer, the following command represents a conceptual way to access
   # the available output. Replace [OMNIVIEWER_OUTPUT_URL] with the actual URL where the output is hosted.
   # For Linux/WSL environments:
   xdg-open "https://omniviewer.example.com/series_record_id/output"
   # For macOS:
   # open "https://omniviewer.example.com/series_record_id/output"

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6. 6

   The "A" set represents the Nxf1-CAST mutants and the "B" set represents the Nxf1-B6 mutants.

   Data Grouping/Sample Annotation (Inferred with models/gemini-2.5-flash) vN/A

   $ Bash example

   # Define sample sets based on description
   # Set A: Nxf1-CAST mutants
   # Set B: Nxf1-B6 mutants
   
   # This step represents the logical grouping of samples based on experimental design.
   # A common way to represent this in a pipeline is through a manifest or sample sheet.
   
   # Create a placeholder manifest file to categorize samples.
   # In a real scenario, this file would be generated from experimental metadata.
   
   # Example: Assuming sample IDs are available and need to be assigned to sets.
   # This script creates a CSV file defining the sets.
   
   echo "Sample_ID,Set_Name,Genotype" > sample_manifest.csv
   echo "Nxf1_CAST_Sample_1,A,Nxf1-CAST" >> sample_manifest.csv
   echo "Nxf1_CAST_Sample_2,A,Nxf1-CAST" >> sample_manifest.csv
   echo "Nxf1_B6_Sample_1,B,Nxf1-B6" >> sample_manifest.csv
   echo "Nxf1_B6_Sample_2,B,Nxf1-B6" >> sample_manifest.csv
   
   echo "Sample manifest 'sample_manifest.csv' created, defining 'A' and 'B' sets."
   
   # No specific reference datasets are directly used in this sample grouping step.
   # Reference genomes (e.g., GRCh38) would be used in subsequent analysis steps (e.g., alignment, peak calling).

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Tools Used