bulk-rnaseq-gsea

To run GSEA on bulk RNA-seq DE results, this pipeline connects limma-voom DE rankings to MSigDB and custom databases, executes enrichment testing, and outputs a unified master_gsea_table.csv for cross-database comparisons.

You can add custom gene-set databases like MitoCarta, TransportDB, and mitoXplorer to your bulk RNA-seq GSEA analysis. The pipeline normalizes these non-MSigDB results into a common 13-column schema for consolidated pathway comparisons.

The bulk RNA-seq GSEA pipeline handles ID mapping and formatting by converting identifiers into T2G and T2N formats, enabling 0-eps GSEA execution and ensuring normalized results across MSigDB and custom databases.

The best way to visualize enriched pathways from multiple GSEA databases is by using the consolidated master_gsea_table.csv, which is specifically formatted for Python visualization scripts and interactive dashboards.

Yes, the GSEA pipeline supports idempotent checkpointing for pathway analysis. It executes MSigDB GSEA via clusterProfiler and fgsea with idempotent master table updates to prevent data loss and ensure reproducible enrichment testing.

Normalizing custom database GSEA results to a 13-column master table standardizes heterogeneous pathway outputs from MSigDB, MitoPathways, and TransportDB into a single schema suitable for cross-database visualization and interactive dashboards.