gsea_for_ranked_gene_list

To run preranked GSEA, provide a ranked gene list file with a gene column and a ranking score column, and the script generates enrichment results and publication-ready plots. It automatically normalizes gene symbols and validates your input.

Gene set enrichment analysis supports MSigDB libraries including HALLMARK, KEGG, REACTOME, and GO (BP, MF, CC) for both human and mouse data. You select the target library when running the script.

Yes, this script uses gseapy to generate downloadable publication-ready GSEA plots. It produces full result tables, significant-result summaries, and visualizations suitable for publication.

Yes, preranked GSEA works with both human and mouse gene expression data. The script includes automatic gene symbol normalization to ensure compatibility across both organisms.

Preranked gene set enrichment analysis requires a ranked gene list containing a gene column and a ranking score column. The script performs input validation to ensure your data meets these requirements.

Yes, you need pandas, numpy, and gseapy installed as Python dependencies to run GSEA on a ranked gene list. These libraries handle data processing and enrichment analysis computations.