microarray-analysis

What problem does it solve?

This skill guides end-to-end analysis of DNA microarray gene expression data from GEO datasets, enabling researchers to perform QC, normalization, differential expression, and functional annotation in a reproducible workflow.

Core Features & Use Cases

• Data loading and QC: Read Affymetrix CEL files or Illumina BeadChip data, assess quality metrics, and prepare data for analysis.
• Normalization and preprocessing: Apply RMA for Affymetrix arrays and quantile normalization for Illumina data, with probe-to-gene mapping.
• Differential expression and annotation: Use limma-based modeling to identify DE genes, map probes to symbols/Entrez IDs, and annotate results.
• Pathway and gene-set analysis: Perform GO and KEGG enrichment, and generate GSEA-compatible plots for top gene sets.
• Output and documentation: Produce tabular results and a set of plots with progressive documentation (analysis.md and plots.md).

Quick Start

Provide GEO study data (GSE accession) and this skill will perform QC, normalization, differential expression analysis, and enrichment across Affymetrix and Illumina microarray datasets.