GSE17538 Processing Pipeline

Publication

Life science alliance (2020) — PMID 32817263

Dataset

Experimentally Derived Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients

1. 1

   Bioconductor's affy package was used for RMA normalization and raw data processing using default settings for background correction and normalization.

   R v4.3.x (Bioconductor 3.18) GitHub

   $ Bash example

   # Install R and the affy Bioconductor package
   # conda create -n affy_env r-base bioconductor-affy -c conda-forge -c bioconda
   # conda activate affy_env
   
   # Create an R script for RMA normalization (e.g., run_rma.R)
   cat << 'EOF' > run_rma.R
   # Load the affy package
   library(affy)
   
   # Define the directory containing CEL files
   # Replace 'path/to/your/cel_files' with the actual path to your Affymetrix CEL files
   cel_files_dir <- "path/to/your/cel_files"
   
   # Read CEL files into an AffyBatch object
   # This function automatically detects CEL files in the specified path.
   data <- ReadAffy(celfile.path = cel_files_dir)
   
   # Perform RMA normalization with default settings
   # The 'rma' function performs background correction, normalization, and summarization
   # using default parameters as described in the affy package documentation.
   eset <- rma(data)
   
   # Optionally, save the normalized expression matrix to a tab-separated file
   # Replace 'rma_normalized_expression.txt' with your desired output file name
   write.exprs(eset, file = "rma_normalized_expression.txt")
   
   # You can also save the ExpressionSet object itself for further R analysis
   # save(eset, file = "rma_normalized_eset.RData")
   EOF
   
   # Execute the R script
   Rscript run_rma.R

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2. 2

   Cox regression hazards model was applied to the processed data using the survival package.

   R (survival package) vR 4.3.2 (survival 3.6-17) (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install R if not already installed (example for Ubuntu/Debian)
   # sudo apt update
   # sudo apt install r-base
   
   # To run the R script:
   # Rscript run_cox_regression.R
   
   # --- run_cox_regression.R content ---
   # Install the 'survival' package if not already installed
   # if (!requireNamespace("survival", quietly = TRUE)) {
   #   install.packages("survival")
   # }
   
   # Load the 'survival' package
   library(survival)
   
   # Load processed data
   # Assuming 'processed_data.csv' is a CSV file with columns:
   # 'time': survival time (numeric)
   # 'event': event indicator (numeric, 0 = censored, 1 = event)
   # 'covariate1', 'covariate2', ...: other covariates for the model
   data <- read.csv("processed_data.csv")
   
   # Ensure 'event' column is numeric (0 or 1)
   data$event <- as.numeric(data$event)
   
   # Apply Cox regression hazards model
   # Replace 'covariate1 + covariate2' with the actual covariates from your data
   # Example with two covariates:
   cox_model <- coxph(Surv(time, event) ~ covariate1 + covariate2, data = data)
   
   # Print summary of the model results
   summary(cox_model)
   
   # Optionally, save the model object or its summary
   # save(cox_model, file = "cox_model.RData")
   # sink("cox_model_summary.txt")
   # summary(cox_model)
   # sink()
   

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3. 3

   All analyses were performed using R software.

   R GitHub

   $ Bash example

   # Install R (example using conda)
   # conda install -c conda-forge r-base
   
   # The description is generic, so a specific R command cannot be inferred.
   # This is a placeholder for a typical R script execution.
   # Rscript your_analysis_script.R --input_file data.csv --output_file results.tsv

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Tools Used