GSE73843 Processing Pipeline

Publication

Nature structural & molecular biology (2016) — PMID 27775709

Dataset

Transcriptome analysis of diverse cell types infected with human cytomegalovirus [array]

1. 1

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

   Microarray vNot specified

   $ Bash example

   # Install Affy Power Tools (APT) if not already installed
   # conda install -c bioconda affy-power-tools
   
   # Example usage of apt-probeset-summarize
   # This command processes CEL files using a specified algorithm (e.g., RMA, MAS5)
   # and a CDF file specific to the array type, outputting a summarized probeset file.
   
   # Create a dummy list of CEL files for demonstration
   # echo "sample1.CEL" > input_cel_files.txt
   # echo "sample2.CEL" >> input_cel_files.txt
   
   # Placeholder for the CDF file specific to your Affymetrix array
   # Replace 'your_array_type.cdf' with the actual CDF file path
   # For example, for Human Gene 1.0 ST arrays, it might be HuGene-1_0-st-v1.cdf
   
   # Make sure the output directory exists
   mkdir -p output_dir
   
   apt-probeset-summarize \
       --cel-files input_cel_files.txt \
       --cdf-file your_array_type.cdf \
       --output-file output_dir/probeset_summary.tsv \
       --probe-set-alg rma \
       --log-file output_dir/apt_log.txt

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2. 2

   Iter-plier algorithm used to quantify probesets.

   affy (R package) v1.78.0 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install R and Bioconductor if not already installed
   # For Bioconductor (current release, e.g., 3.19 for R 4.3)
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")'
   # R -e 'BiocManager::install("affy")'
   
   # Example R script to quantify probesets using the PLIER algorithm via the 'affy' package
   # Assuming input CEL files are in a directory named 'cel_files'
   # and output will be written to 'quantified_probesets.tsv'
   
   # Create an R script file
   cat << 'EOF' > run_plier_quantification.R
   library(affy)
   
   # --- Configuration ---
   cel_dir <- "cel_files" # Directory containing your CEL files
   output_file <- "quantified_probesets.tsv" # Desired output file name
   
   # --- Input Validation ---
   if (!dir.exists(cel_dir)) {
     stop(paste("Error: CEL file directory not found:", cel_dir))
   }
   cel_files <- list.files(path = cel_dir, pattern = "\\.CEL$", full.names = TRUE, ignore.case = TRUE)
   if (length(cel_files) == 0) {
     stop(paste("Error: No CEL files found in", cel_dir, ". Please ensure files end with .CEL (case-insensitive)."))
   }
   
   cat("Found", length(cel_files), "CEL files.\n")
   
   # --- Read CEL files into an AffyBatch object ---
   # This step might require a specific CDF environment for certain array types.
   # For common arrays, ReadAffy can often infer.
   # If you encounter errors related to CDF, you might need to install a specific
   # Bioconductor annotation package (e.g., 'hgu133plus2.db') and specify it.
   # Example: raw_data <- ReadAffy(filenames = cel_files, cdfname = "hgu133plus2")
   # For this example, we assume ReadAffy can proceed without explicit CDF specification.
   tryCatch({
     raw_data <- ReadAffy(filenames = cel_files)
   }, error = function(e) {
     stop(paste("Error reading CEL files into AffyBatch:", e$message,
                "\nConsider checking your CEL files or specifying a CDF environment."))
   })
   
   cat("Successfully loaded raw Affymetrix data.\n")
   
   # --- Quantify probesets using the PLIER algorithm (just.plier function) ---
   # The 'just.plier' function from the 'affy' package implements the PLIER algorithm.
   # It performs background correction, normalization, and summarization.
   cat("Performing probeset quantification using the PLIER algorithm...\n")
   eset_plier <- just.plier(raw_data)
   
   # --- Extract expression matrix ---
   expression_matrix <- exprs(eset_plier)
   
   # --- Write results to a TSV file ---
   cat("Writing quantified probesets to:", output_file, "\n")
   write.table(expression_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE)
   
   cat("Probeset quantification using PLIER completed successfully.\n")
   EOF
   
   # Execute the R script
   Rscript run_plier_quantification.R
   
   # Clean up the R script (optional)
   # rm run_plier_quantification.R

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3. 3

   As previously described (Huelga et al., 2012).

   (Inferred with models/gemini-2.5-flash) vN/A GitHub

   $ Bash example

   # The specific tool, version, and command cannot be inferred from the generic description 'As previously described (Huelga et al., 2012)' without further context about the assay or step type.

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4. 4

   HJAY_r2.pgf

   HJAY_r2.pgf (Inferred with models/gemini-2.5-flash) vr2

   $ Bash example

   bash
   # This command is a generic placeholder as the step description "HJAY_r2.pgf"
   # does not provide sufficient information to infer specific parameters or
   # the exact nature of the script. The file extension '.pgf' typically refers
   # to Portable Graphics Format, suggesting this might be a plotting script or
   # an internal identifier rather than a standard bioinformatics tool.
   # Replace 'input_data.txt' and 'output_result.txt' with actual file names.
   ./HJAY_r2.pgf --input input_data.txt --output output_result.txt
   

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Tools Used