massiveGST

massiveGST applies the Mann-Whitney-Wilcoxon test to gene-set enrichment analysis (GSEA) to evaluate enrichment in transcriptomic data.

Key Features:

• Competitive methodology: Employs the Mann-Whitney-Wilcoxon test as a competitive GSEA approach that is independent of the source of gene profiles and does not rely on global-test assumptions.
• Normalized Enrichment Score (NES): Computes a Normalized Enrichment Score that provides both a probability estimate and a location index for interpreting gene-set enrichment.
• R package implementation: Provides an R package implementation for integration into bioinformatics workflows.
• Reproducible outputs: Generates tabular and graphical outputs to support reproducible analyses and transparent reporting of results.

Scientific Applications:

• Transcriptomic gene-set enrichment: Assess enrichment of gene sets in transcriptomic datasets using a competitive statistical test.
• Identification of differentially expressed genes: Support identification and interpretation of differentially expressed genes across conditions.
• Pathway analysis: Characterize biological pathways involved in diseases or treatments from gene-expression data.
• Comparative expression profiling: Compare gene expression profiles between experimental groups to derive biological insights.

Methodology:

Applies the Mann-Whitney-Wilcoxon test to GSEA and normalizes enrichment scores to produce a Normalized Enrichment Score (NES).