GSE86464 Processing Pipeline

Publication

Neuron (2016) — PMID 27773581

Dataset

HNRNPA2B1 regulates alternative RNA processing in the nervous system and accumulates in granules in ALS IPSC-derived motor neurons

1. 1

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

   Microarray vNot specified (Inferred with models/gemini-2.5-flash)

   $ Bash example

   # Install Affy Power Tools (APT) via Bioconda
   # conda install -c bioconda affy-power-tools
   
   # Example usage of apt-probeset-summarize
   # This command processes Affymetrix CEL files to generate summarized probe set data.
   # Replace 'path/to/library.cdf', 'path/to/input_cel_file_1.CEL', etc., and 'path/to/output_dir' with actual paths.
   # The --analysis parameter specifies the summarization algorithm (e.g., rma, mas5, plier).
   # The --output-dir parameter specifies where to write the output files.
   
   apt-probeset-summarize \
       --cdf-file path/to/library.cdf \
       --analysis rma \
       --cel-files path/to/input_cel_file_1.CEL path/to/input_cel_file_2.CEL \
       --output-dir path/to/output_dir

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2. 2

   Iter-plier algorithm used to quantify probesets.

   affy (Inferred with models/gemini-2.5-flash) v1.78.0 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 to perform Iter-plier background correction and RMA quantification
   cat << 'EOF' > quantify_probesets.R
   library(affy)
   
   # Define input and output directories
   # Assumes raw Affymetrix .CEL files are located in the 'raw_cel_files' directory.
   # Create this directory and place your .CEL files there before running.
   input_dir <- "raw_cel_files"
   output_dir <- "quantified_results"
   dir.create(output_dir, showWarnings = FALSE)
   
   # Check if input directory exists
   if (!dir.exists(input_dir)) {
     stop("Input directory '", input_dir, "' not found. Please create it and place .CEL files inside.")
   }
   
   # List all .CEL files in the input directory
   cel_files <- list.celfiles(path=input_dir, full.names=TRUE)
   
   if (length(cel_files) == 0) {
     stop("No .CEL files found in the input directory: ", input_dir)
   }
   print(paste("Found", length(cel_files), ".CEL files for quantification."))
   
   # Read the .CEL files into an AffyBatch object
   # This step reads raw intensity data from the arrays.
   raw_data <- ReadAffy(filenames=cel_files)
   
   # Perform IterPLIER background correction
   # The IterPLIER algorithm is used to estimate and subtract background noise.
   # This function is part of the 'affy' package.
   bg_corrected_affybatch <- bg.correct.iterplier(raw_data)
   
   # Perform RMA (Robust Multi-array Average) normalization and summarization
   # RMA is a widely used method for normalizing and summarizing Affymetrix GeneChip data.
   # This step quantifies probesets by combining probe intensities into a single expression value per probeset.
   eset <- rma(bg_corrected_affybatch)
   
   # Extract the expression matrix (quantified probeset values)
   expression_matrix <- exprs(eset)
   
   # Get probeset IDs
   probeset_ids <- featureNames(eset)
   
   # Combine probeset IDs with the expression matrix into a data frame
   quantified_data <- data.frame(ProbesetID = probeset_ids, expression_matrix)
   
   # Define the output file path
   output_file <- file.path(output_dir, "probeset_quantification_iterplier_rma.csv")
   
   # Write the quantified probeset data to a CSV file
   write.csv(quantified_data, output_file, row.names = FALSE)
   
   print(paste("Probeset quantification complete. Results saved to:", output_file))
   EOF
   
   # Create input directory for CEL files (if it doesn't exist)
   mkdir -p raw_cel_files
   
   # Execute the R script to perform quantification
   Rscript quantify_probesets.R
   
   # Example of creating dummy CEL files for testing (optional)
   # This part is for demonstration if you don't have actual CEL files.
   # It will create empty files, which 'ReadAffy' will likely fail on,
   # but shows the expected input structure.
   # touch raw_cel_files/sample_A.CEL
   # touch raw_cel_files/sample_B.CEL
   

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3. 3

   http://exon.ucsc.edu/documentation/mjay_library/hjay.pgf

   clipper (Inferred with models/gemini-2.5-flash) vNot specified (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install clipper (if not already installed)
   # git clone https://github.com/yeolab/clipper.git
   # cd clipper
   # python setup.py install # Or just run the script directly
   
   # Example usage for eCLIP peak calling with clipper
   # Assuming input BAM files and a control BAM file are available
   # Replace with actual file paths and species
   INPUT_BAM="input.bam"
   CONTROL_BAM="control.bam"
   OUTPUT_PREFIX="peaks"
   SPECIES="hg38" # Placeholder for latest assembly
   
   # Run clipper
   python clipper.py \
       -o "${OUTPUT_PREFIX}.bed" \
       -s "${SPECIES}" \
       -b "${INPUT_BAM}" \
       -c "${CONTROL_BAM}" \
       --bonferroni \
       --fdr 0.05 \
       --window 100 \
       --step 20

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Tools Used