bio-workflows-expression-to-pathways

Pathway enrichment analysis on RNA-seq differential expression results is performed by inputting DE gene lists into a workflow that runs GO, KEGG, and Reactome enrichment using clusterProfiler, generating enriched terms and publication-ready plots.

Yes, you can run gene-set enrichment analysis (GSEA) on a ranked gene list from differential expression data, interpreting ranked DE data to identify enriched pathways and produce visualization plots across GO, KEGG, and Reactome databases.

The best way to convert DE gene lists for GO and KEGG enrichment analysis involves applying ID conversion procedures that ensure valid gene identifiers pass QC checkpoints before clusterProfiler executes the enrichment queries.

Yes, this pathway enrichment workflow supports Reactome database visualization, generating publication-ready plots for Reactome enriched terms alongside standard GO and KEGG enrichment outputs for RNA-seq DE results.

GO enrichment analysis returns no meaningful terms for a DE gene list when input gene IDs fail QC checkpoints, requiring proper ID conversion to valid identifiers before clusterProfiler can successfully execute enrichment queries.

Yes, you need clusterProfiler to generate publication-ready plots from pathway enrichment, as it drives the underlying enrichment analyses and optional visualization exports for GO, KEGG, and Reactome results.