GSE84105 Processing Pipeline
GSE
code_examples
1 step
Publication
Heterogenous Populations of Tissue-Resident CD8<sup>+</sup> T Cells Are Generated in Response to Infection and Malignancy.Immunity (2020) — PMID 32433949
Dataset
GSE84105Defining memory-like CD8 T cells that respond to PD-1 therapy in chronic viral infection
Warning: Pipeline descriptions and code snippets may be inferred or AI-generated. Use them only as a starting point to guide analysis, and validate before use.
Processing Steps
Generate Jupyter Notebook-
1
Data was normalized using RMAExpress.
RMAExpress vNot specified (Inferred with models/gemini-2.5-flash)$ Bash example
# The original RMAExpress is a GUI application. This code block demonstrates how to perform Robust Multi-array Average (RMA) normalization using the 'affy' Bioconductor package in R, which implements the same algorithm. # Install R and Bioconductor if not already present # R # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("affy") # Create an R script for RMA normalization cat << 'EOF' > normalize_rma.R library(affy) # Get CEL file paths from command line arguments # The first argument is expected to be the directory containing CEL files. args <- commandArgs(trailingOnly = TRUE) if (length(args) == 0) { stop("Please provide the path to the directory containing CEL files as the first argument.", call. = FALSE) } cel_dir <- args[1] output_file <- "rma_normalized_data.txt" # List all .CEL files in the specified directory cel_files <- list.files(path = cel_dir, pattern = "\\.CEL$", full.names = TRUE) if (length(cel_files) == 0) { stop(paste("No .CEL files found in directory:", cel_dir), call. = FALSE) } message(paste("Found", length(cel_files), "CEL files in", cel_dir)) message("Reading CEL files...") data <- ReadAffy(filenames = cel_files) message("Performing RMA normalization...") eset <- rma(data) # Extract normalized expression matrix normalized_matrix <- exprs(eset) # Write normalized data to a tab-separated file write.table(normalized_matrix, file = output_file, sep = "\t", quote = FALSE, row.names = TRUE) message(paste("RMA normalized data written to", output_file)) EOF # Execute the R script # Replace 'path/to/your/cel_files_directory' with the actual directory containing your .CEL files Rscript normalize_rma.R path/to/your/cel_files_directory
Raw Source Text
Data was normalized using RMAExpress.