GSE54969 Processing Pipeline

Publication

Circulation (2015) — PMID 25739401

Dataset

Transcriptomic analysis reveals novel long non-coding RNAs critical for vertebrate development

1. 1

   CEL files were analyzed using the oligo package in R/Bioconductor and normalized using RMA

   R vNot specified, typically tied to Bioconductor releases GitHub

   $ Bash example

   # Install Bioconductor and oligo package if not already installed
   # R -e 'if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")'
   # R -e 'BiocManager::install("oligo")'
   
   # Create a dummy directory for CEL files for demonstration (replace with actual CEL file path)
   # mkdir -p cel_files
   # touch cel_files/sample1.CEL
   # touch cel_files/sample2.CEL
   
   # R script for RMA normalization using oligo package
   Rscript -e '
       library(oligo)
   
       # Define the directory containing CEL files
       # IMPORTANT: Replace "cel_files" with the actual path to your CEL files
       cel_files_dir <- "cel_files"
   
       # List all CEL files in the directory
       cel_files <- list.files(cel_files_dir, pattern = "\\.CEL$", full.names = TRUE)
   
       # Check if any CEL files were found
       if (length(cel_files) == 0) {
           stop("No CEL files found in the specified directory: ", cel_files_dir)
       }
   
       # Read CEL files
       raw_data <- read.celfiles(cel_files)
   
       # Perform RMA normalization
       normalized_data <- rma(raw_data)
   
       # Extract expression matrix
       expression_matrix <- exprs(normalized_data)
   
       # Save the normalized expression matrix to a CSV file
       write.csv(expression_matrix, "rma_normalized_expression.csv", row.names = TRUE)
   
       message("RMA normalization complete. Output saved to rma_normalized_expression.csv")
   '

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