GSE86038 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 [hnRNPA2B1_Arrays_…

1. 1

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

   Microarray vNot specified

   $ Bash example

   # Install Affymetrix Power Tools (APT)
   # APT is typically downloaded as a binary package from the Thermo Fisher Scientific website.
   # Example installation (may vary based on OS and APT version):
   # wget https://assets.thermofisher.com/TFS-Assets/LSG/software/APT_2.x.x_Linux.zip
   # unzip APT_2.x.x_Linux.zip
   # export PATH=$PATH:/path/to/APT_binaries
   
   # Placeholder variables
   # Replace with actual CEL files. For multiple files, list them space-separated.
   # Example: INPUT_CEL_FILES="sample1.CEL sample2.CEL sample3.CEL"
   # Alternatively, for a long list, create a file (e.g., cel_list.txt) with one CEL file path per line
   # and use --cel-files-file cel_list.txt instead of --cel-files.
   INPUT_CEL_FILES="sample1.CEL sample2.CEL sample3.CEL"
   # Replace with the appropriate library file (e.g., CDF, PGF) for your array type
   PROBESET_LIBRARY_FILE="HG-U133_Plus_2.cdf"
   OUTPUT_DIR="apt_summarize_output"
   # Common algorithms: rma, mas5, plier, etc. Choose based on experimental design.
   ALGORITHM="rma"
   
   mkdir -p "${OUTPUT_DIR}"
   
   apt-probeset-summarize \
     --cel-files ${INPUT_CEL_FILES} \
     --cdf-file "${PROBESET_LIBRARY_FILE}" \
     --output-dir "${OUTPUT_DIR}" \
     --analysis-algorithm "${ALGORITHM}" \
     --log-file "${OUTPUT_DIR}/apt_summarize.log"

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2. 2

   Iter-plier algorithm used to quantify probesets.

   plier (Inferred with models/gemini-2.5-flash) vBioconductor (e.g., 1.78.0 for R 4.3) GitHub

   $ Bash example

   # Install R and Bioconductor if not already present
   # For example, using conda:
   # conda create -n r_env r-base bioconductor-affy bioconductor-plier
   # conda activate r_env
   
   # Create an R script to perform Iter-PLIER quantification
   cat << 'EOF' > quantify_probesets.R
   # Load necessary Bioconductor packages
   library(affy)
   library(plier)
   
   # Define input and output paths
   # Assuming CEL files are in a directory named 'cel_files'
   cel_dir <- "cel_files"
   output_file <- "probeset_quantification_iterplier.tsv"
   
   # Get list of 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: ", cel_dir)
   }
   
   # Read CEL files into an AffyBatch object
   # This step automatically handles the appropriate CDF (Chip Description File)
   # for the array type if available in Bioconductor annotation packages.
   raw_data <- ReadAffy(filenames = cel_files)
   
   # Perform PLIER quantification, which is an iterative algorithm (Iter-PLIER).
   # The 'plier' function from the 'plier' package implements this algorithm.
   es_plier <- plier(raw_data)
   
   # Extract expression matrix
   expression_matrix <- exprs(es_plier)
   
   # Write results to a TSV file
   write.table(expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message(paste("Probeset quantification completed. Results saved to:", output_file))
   EOF
   
   # Execute the R script
   Rscript quantify_probesets.R
   
   # Example directory structure for input CEL files:
   # mkdir -p cel_files
   # touch cel_files/sample1.CEL
   # touch cel_files/sample2.CEL

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3. 3

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

   clipper (Inferred with models/gemini-2.5-flash) vlatest (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
   # pip install .
   
   # Placeholder for genome reference files (e.g., hg38)
   # mkdir -p reference_data
   # wget -P reference_data http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
   # gunzip reference_data/hg38.fa.gz
   # samtools faidx reference_data/hg38.fa
   
   # Define input files (placeholders)
   IP_BAM="ip_sample.bam"
   CONTROL_BAM="control_sample.bam"
   GENOME_FASTA="reference_data/hg38.fa"
   OUTPUT_PREFIX="eclip_peaks"
   
   # Run clipper for peak calling
   python clipper.py -b "${IP_BAM}" -c "${CONTROL_BAM}" -g "${GENOME_FASTA}" -o "${OUTPUT_PREFIX}"

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Tools Used