GSE54321 Processing Pipeline

Publication

Nature immunology (2014) — PMID 24584088

Dataset

Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene expression analyses

1. 1

   The Ct values in the Matrix non-normalized were exported from the BioMark software.

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

   $ Bash example

   # This step involves exporting Ct values from the BioMark software's graphical user interface.
   # There is no direct command-line execution for the export operation itself from this proprietary software.
   # The following command is a placeholder representing the handling of the exported file
   # after it has been manually generated by the BioMark software.
   # It assumes the exported file is named 'biomark_ct_matrix_non_normalized.csv'
   # and moves it to a designated raw data directory for further processing.
   
   # Create a directory for raw data if it doesn't exist
   mkdir -p ./data/raw_ct_values
   
   # Move the exported Ct values file to the raw data directory
   mv /path/to/biomark_export/biomark_ct_matrix_non_normalized.csv ./data/raw_ct_values/biomark_ct_matrix_non_normalized.csv

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2. 2

   The Ct values are single-cell gene expression measurements, therefore were not normalized.

   Python (Inferred with models/gemini-2.5-flash) v3.x

   $ Bash example

   bash
   # This script represents a conceptual step where single-cell Ct values are handled.
   # As per the description, these values are not normalized.
   # This step might involve loading, validating, or simply passing through the raw Ct data
   # for downstream analysis, without applying any normalization procedures.
   
   # Install Python and pandas if not available
   # conda install -c anaconda python=3.9 pandas
   
   # Create a dummy Python script to process (or rather, pass through) Ct values
   cat << 'EOF' > process_ct_values.py
   import pandas as pd
   import sys
   
   if len(sys.argv) != 3:
       print("Usage: python process_ct_values.py <input_ct_file> <output_unnormalized_ct_file>", file=sys.stderr)
       sys.exit(1)
   
   input_file = sys.argv[1]
   output_file = sys.argv[2]
   
   print(f"Loading single-cell Ct values from {input_file}...")
   try:
       df_ct = pd.read_csv(input_file)
       print(f"Successfully loaded {df_ct.shape[0]} rows and {df_ct.shape[1]} columns.")
       print("As per pipeline design, these Ct values are NOT normalized.")
       
       # Save the data to the output file without any normalization
       df_ct.to_csv(output_file, index=False)
       print(f"Unnormalized Ct values saved to {output_file}.")
   except FileNotFoundError:
       print(f"Error: Input file '{input_file}' not found.", file=sys.stderr)
       sys.exit(1)
   except Exception as e:
       print(f"Error processing Ct values: {e}", file=sys.stderr)
       sys.exit(1)
   EOF
   
   # Create a dummy input file for demonstration
   echo "cell_id,geneA_Ct,geneB_Ct" > single_cell_ct_values.csv
   echo "C1,25.1,28.5" >> single_cell_ct_values.csv
   echo "C2,24.9,29.1" >> single_cell_ct_values.csv
   
   # Execute the Python script to handle Ct values without normalization
   # The script will read the input, confirm no normalization, and write to output.
   python process_ct_values.py single_cell_ct_values.csv single_cell_ct_values_unnormalized.csv
   
   # Clean up dummy files (optional)
   # rm single_cell_ct_values.csv
   # rm single_cell_ct_values_unnormalized.csv
   # rm process_ct_values.py
   
   # Reference datasets: Not applicable for this descriptive step about raw Ct values.
   # If downstream analysis requires a reference, it would be specified in that step.
   

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3. 3

   Fold-changes were not calculated.

   DESeq2 (Inferred with models/gemini-2.5-flash) v1.38.3 GitHub

   $ Bash example

   # This step indicates that fold-changes were explicitly not calculated in this analysis.
   # Therefore, no differential expression/binding tool like DESeq2 was executed for this purpose.
   echo "Fold-changes were not calculated as per the analysis design."

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