GSE17536 Processing Pipeline

Publication

Life science alliance (2020) — PMID 32817263

Dataset

Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (Moffitt Samples)

1. 1

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

   R vR 4.3.x, affy 1.78.0 GitHub

   $ Bash example

   # Install R and Bioconductor (if not already installed)
   # For Ubuntu/Debian:
   # sudo apt update
   # sudo apt install r-base
   # R -e "install.packages('BiocManager')"
   # R -e "BiocManager::install('affy')"
   # R -e "BiocManager::install('hgu133plus2.db')" # Example annotation package for a common chip, replace with actual chip type if known
   
   # Create a dummy directory for CEL files for demonstration
   mkdir -p cel_files
   # For a real run, place your actual .CEL files into the 'cel_files' directory.
   # Example: cp /path/to/your/sample1.CEL cel_files/
   # Example: cp /path/to/your/sample2.CEL cel_files/
   
   # R script to perform RMA normalization
   Rscript -e '
     library(affy)
     
     # Define the directory containing CEL files
     cel_dir <- "cel_files"
     
     # Read CEL files from the specified directory
     # This assumes all CEL files in the directory belong to the same experiment
     # and are compatible with the same chip type. If a specific chip type
     # CDF package is installed (e.g., hgu133plus2.db), you can specify it:
     # affybatch <- ReadAffy(celfile.path=cel_dir, cdfname="hgu133plus2")
     affybatch <- ReadAffy(celfile.path=cel_dir)
     
     # Perform RMA normalization with default settings
     # Default settings for rma: background.correct=TRUE, normalize=TRUE, verbose=TRUE
     eset <- rma(affybatch)
     
     # Extract normalized expression matrix
     normalized_data <- exprs(eset)
     
     # Save the normalized data to a CSV file
     write.csv(normalized_data, "rma_normalized_expression.csv", row.names = TRUE)
     
     # Optionally, save the ExpressionSet object for further analysis
     # save(eset, file="rma_eset.RData")
     
     message("RMA normalization complete. Normalized data saved to rma_normalized_expression.csv")
   '
   

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2. 2

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

   R (survival package) vR 4.2.0, survival 3.5-7 (Inferred with models/gemini-2.5-flash) GitHub

   $ Bash example

   # Install R if not already installed (example for Ubuntu)
   # sudo apt update
   # sudo apt install r-base
   
   # Create an R script for Cox regression
   cat << 'EOF' > run_cox_regression.R
   # Load the survival package
   # If the package is not installed, it will attempt to install it.
   if (!requireNamespace("survival", quietly = TRUE)) {
       install.packages("survival", repos='http://cran.us.r-project.org')
   }
   library(survival)
   
   # Load processed data (replace 'processed_data.csv' with your actual file path)
   # This example assumes a CSV file with columns: time, event (0=censored, 1=event), and covariates.
   # Adjust column names and data loading based on your specific data format.
   # Example data structure:
   # time,event,covariate1,covariate2
   # 10,1,2.5,A
   # 15,0,3.1,B
   # 20,1,1.8,A
   # ...
   # Placeholder for input data. You MUST replace 'processed_data.csv' with your actual input file.
   data <- read.csv("processed_data.csv")
   
   # Ensure 'time' and 'event' are numeric
   data$time <- as.numeric(data$time)
   data$event <- as.numeric(data$event)
   
   # Create a Surv object
   surv_object <- Surv(time = data$time, event = data$event)
   
   # Apply Cox regression hazards model
   # Replace 'covariate1 + covariate2' with your actual covariates from the 'data' dataframe.
   # Example model: coxph(surv_object ~ age + sex + treatment, data = data)
   cox_model <- coxph(surv_object ~ covariate1 + covariate2, data = data)
   
   # Print model summary to standard output
   summary(cox_model)
   
   # Optionally, save the full summary to a text file
   sink("cox_regression_summary.txt")
   print(summary(cox_model))
   sink()
   
   # Optionally, save the Cox model object for later use (e.g., prediction, plotting)
   saveRDS(cox_model, "cox_model.rds")
   EOF
   
   # Execute the R script
   Rscript run_cox_regression.R

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3. 3

   All analyses were performed using R software.

   R GitHub

   $ Bash example

   # This step indicates that R software was used for analyses.
   # The specific R script and parameters are not provided in the description.
   # A placeholder command for running an R script is shown below.
   # Replace 'your_analysis_script.R' with the actual script used.
   # Replace 'arg1 arg2' with any specific arguments.
   Rscript your_analysis_script.R # Add any specific arguments here

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Tools Used