GSE16681 Processing Pipeline

Publication

Stem cells and development (2010) — PMID 19807270

Dataset

mRNA expression data from differentiation of human ESCs into definitive endoderm, Cyt49 on matrigel

1. 1

   The data were normalised using quantile normalisation with IlluminaGUI in R

   R vNot specified GitHub

   $ Bash example

   # Install R and limma if not already present
   # conda install -c conda-forge r-base
   # conda install -c bioconda bioconductor-limma
   
   # Create a dummy R script for quantile normalization
   # This script assumes 'input_data.tsv' contains the data matrix
   # and will output 'normalized_data.tsv'
   cat << 'EOF' > normalize_data.R
   # Load necessary library
   library(limma)
   
   # --- Configuration ---
   input_file <- "input_data.tsv" # Placeholder for input data file
   output_file <- "normalized_data.tsv" # Placeholder for output data file
   
   # --- Load Data ---
   # Assuming input_data.tsv is a tab-separated file with header
   # and the first column is gene/feature IDs, and subsequent columns are samples
   # Adjust read.delim parameters based on actual file format
   data_matrix <- as.matrix(read.delim(input_file, row.names = 1, sep = "\t", header = TRUE))
   
   # --- Perform Quantile Normalization ---
   # The 'method="quantile"' argument specifies quantile normalization
   normalized_matrix <- normalizeBetweenArrays(data_matrix, method = "quantile")
   
   # --- Save Normalized Data ---
   # Write the normalized matrix to a new tab-separated file
   write.table(normalized_matrix, file = output_file, sep = "\t", quote = FALSE, col.names = NA)
   
   message(paste("Quantile normalization complete. Normalized data saved to:", output_file))
   EOF
   
   # Create a dummy input file for demonstration
   echo -e "Gene\tSample1\tSample2\tSample3" > input_data.tsv
   echo -e "GeneA\t100\t120\t90" >> input_data.tsv
   echo -e "GeneB\t50\t60\t45" >> input_data.tsv
   echo -e "GeneC\t200\t210\t180" >> input_data.tsv
   echo -e "GeneD\t75\t80\t70" >> input_data.tsv
   
   # Execute the R script
   Rscript normalize_data.R

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