bulk-rnaseq-gsea-master-tables

To combine GSEA results into a master table, this Skill normalizes heterogeneous gseaResult objects from MSigDB and custom databases into a consistent 13-column schema. It appends outputs idempotently to produce a unified master_gsea_table.csv without duplicates.

R and Python GSEA outputs have column mismatches due to schema differences across consumers. This Skill bridges R-based normalization with Python-based visualization by enforcing schema validation and aligning columns into a stable master_gsea_table.csv format.

To prevent duplicates when appending GSEA results, this Skill implements an idempotent append mechanism with schema validation. This ensures reproducible results across re-runs without creating duplicate rows when adding new databases or modules.

Yes, you can generate derived tables from a unified GSEA table. This Skill supports creating outputs such as master_gsea_significant.csv and gsea_summary_stats.csv to prepare filtered data for downstream visualization and reproducible reporting.

The best way to normalize clusterProfiler GSEA outputs is to apply a consistent schema transformation. This Skill converts R-based gseaResult objects into a standardized 13-column CSV format, enabling seamless Python-based visualization and reporting.