GSE39873 Processing Pipeline

Publication

Molecular cell (2012) — PMID 22959275

Dataset

LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance

1. 1

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

   Microarray vInferred with models/gemini-2.5-flash

   $ Bash example

   # Install Affymetrix Power Tools (APT)
   # APT is a proprietary software suite from Thermo Fisher Scientific. Installation typically involves downloading the suite from their official website.
   # Example (conceptual, actual installation may vary based on OS and APT version):
   # wget https://assets.thermofisher.com/TFS-Assets/LSG/software/APT_2.10.2_Linux.zip
   # unzip APT_2.10.2_Linux.zip
   # export PATH=$PATH:/path/to/apt/bin
   
   # Define input CEL files (replace with actual file paths for your experiment)
   # These are the raw data files generated by Affymetrix arrays.
   CEL_FILES="sample1.CEL sample2.CEL sample3.CEL"
   
   # Define output directory for summarization results
   OUTPUT_DIR="apt_summarize_output"
   mkdir -p "${OUTPUT_DIR}"
   
   # Define the CDF file for the specific array type (replace with actual path to your CDF file)
   # The CDF (Chip Description File) is crucial for defining probe sets and is usually downloaded from Affymetrix or Bioconductor.
   # Example for a common array type (e.g., Human Gene 1.0 ST array):
   # CDF_FILE="/path/to/HuGene-1_0-st-v1.cdf"
   # For demonstration, using a placeholder. Ensure you use the correct CDF for your array.
   CDF_FILE="path/to/your/array_type.cdf"
   
   # Run apt-probeset-summarize using the RMA (Robust Multi-array Average) algorithm
   # -a rma: Specifies the RMA algorithm for summarization, a common and robust method.
   # -o ${OUTPUT_DIR}: Specifies the output directory where summarized data will be stored.
   # -c ${CDF_FILE}: Specifies the CDF file to define probe sets for summarization.
   # --cel-files ${CEL_FILES}: Specifies the input CEL files to be processed.
   apt-probeset-summarize -a rma -o "${OUTPUT_DIR}" -c "${CDF_FILE}" --cel-files ${CEL_FILES}
   
   echo "Probeset summarization complete. Results are in ${OUTPUT_DIR}"

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2. 2

   Iter-plier algorithm used to quantify probesets.

   iterPlier v1.78.0

   $ Bash example

   # Install R and Bioconductor if not already present
   # sudo apt-get update
   # sudo apt-get install -y r-base
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager", repos="https://cloud.r-project.org")'
   # R -e 'BiocManager::install(c("affy", "iterPlier"))'
   # R -e 'BiocManager::install("hgu133plus2.db")' # Placeholder: Install the appropriate array-specific CDF package (e.g., for Affymetrix Human Genome U133 Plus 2.0 Array)
   
   # Create an R script for iter-plier quantification
   cat << 'EOF' > iter_plier_quantification.R
   #!/usr/bin/env Rscript
   
   # Parse command line arguments
   args <- commandArgs(trailingOnly = TRUE)
   if (length(args) < 2) {
     stop("Usage: Rscript iter_plier_quantification.R <cel_files_dir> <output_file>\nExample: Rscript iter_plier_quantification.R ./raw_cel_files expression_matrix.tsv", call.=FALSE)
   }
   
   cel_files_dir <- args[1]
   output_file <- args[2]
   
   # Load necessary libraries
   # Ensure 'affy' and 'iterPlier' packages are installed via BiocManager
   library(affy)
   library(iterPlier)
   
   # List CEL files in the specified directory
   cel_files <- list.celfiles(cel_files_dir, full.names = TRUE)
   
   if (length(cel_files) == 0) {
     stop(paste("No CEL files found in:", cel_files_dir), call.=FALSE)
   }
   
   message(paste("Found", length(cel_files), "CEL files. Reading data..."))
   
   # Read CEL files into an AffyBatch object
   # This step requires the appropriate CDF environment to be installed (e.g., hgu133plus2.db)
   raw_data <- ReadAffy(filenames = cel_files)
   
   message("Quantifying probesets using iterPlier...")
   
   # Perform quantification using the iterPlier function
   # This function performs background correction, normalization, and summarization.
   # It returns an ExpressionSet object. The CDF information is inferred from the AffyBatch object.
   expression_set <- iterPlier(raw_data)
   
   # Extract expression matrix (log2 transformed intensities)
   expression_matrix <- exprs(expression_set)
   
   # Write results to a tab-separated file
   write.table(expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   message(paste("Quantification complete. Results written to:", output_file))
   EOF
   
   # Make the R script executable
   chmod +x iter_plier_quantification.R
   
   # Example usage:
   # Create a dummy directory for CEL files (replace with actual path)
   # mkdir -p /path/to/your/cel_files_directory
   # Create dummy CEL files for demonstration (replace with actual CEL files)
   # touch /path/to/your/cel_files_directory/sample1.CEL
   # touch /path/to/your/cel_files_directory/sample2.CEL
   
   # Run the R script
   # Replace /path/to/your/cel_files_directory with the actual directory containing CEL files
   # Replace output_expression.tsv with your desired output file name
   ./iter_plier_quantification.R /path/to/your/cel_files_directory output_expression.tsv

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3. 3

   HJAY_r2.pgf

   Custom Process (Inferred with models/gemini-2.5-flash) vr2

   $ Bash example

   # This command is a placeholder for a custom bioinformatics process identified as HJAY_r2.pgf.
   # No specific tool, parameters, or input/output files could be inferred from the description.
   # If a reference genome is required, 'hg38' is used as a common placeholder.
   # Replace 'custom_hj_tool' with the actual executable and adjust parameters as needed.
   
   # Example: custom_hj_tool --input_file data.txt --output_file HJAY_r2.pgf --genome_assembly hg38
   echo "Executing custom process HJAY_r2.pgf..."

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Tools Used