microarray-analysis

To analyze GEO microarray data, you provide a GSE accession number to trigger an end-to-end pipeline that performs QC, normalization, differential expression analysis, and functional annotation. The workflow outputs organized result tables and figures.

limma works with both Affymetrix and Illumina microarray data by applying RMA normalization for Affymetrix CEL files and quantile normalization for Illumina BeadChip data, supporting paired and unpaired experimental designs.

To perform GO and KEGG enrichment on differentially expressed genes, the pipeline uses limma-based modeling to identify DE genes, maps probes to symbols and Entrez IDs, and generates GSEA-compatible plots for top gene sets.

You need R with limma and platform-specific annotation packages to process microarray gene expression data. These dependencies are required to execute the normalization, differential expression, and probe-to-gene mapping steps.

The best way to normalize Affymetrix CEL files for differential expression analysis is applying RMA normalization within the pipeline, followed by limma-based modeling to identify DE genes and map probes to gene annotations.

Your microarray analysis output includes analysis.md and plots.md files to provide progressive documentation of the workflow. These files organize the tabular results and generated QC, DE, and enrichment figures for downstream interpretation.