bio-pathway-gsea

Gene set enrichment analysis (GSEA) evaluates predefined gene sets to determine coordinated expression changes across a ranked list. Unlike over-representation analysis, GSEA does not require arbitrary cutoffs, allowing it to detect subtle pathway-level shifts across the entire ranked gene list.

You can rank genes using log2 fold-change, signed p-value, or the Wald statistic. The Skill supports these multiple ranking statistics to generate the ranked list required by clusterProfiler's gseGO and gseKEGG functions for pathway enrichment analysis.

Running gseGO and gseKEGG requires an appropriate organism database to map ranked genes to predefined gene sets. You must also ensure gene IDs are correctly formatted to match the chosen database for accurate pathway enrichment results.

Yes, GSEA is applicable for analyzing treatment contrasts across time-course experiments. By evaluating predefined gene sets against ranked gene lists derived from these contrasts, researchers can identify enriched GO and KEGG pathways showing coordinated expression changes.

GSEA avoids arbitrary cutoffs by scoring predefined gene sets across the entire ranked gene list rather than filtering for significantly differentially expressed genes. This approach detects subtle, coordinated expression changes across pathways that threshold-based methods often miss.