mvGST

mvGST integrates one-sided p-values from a matrix using meta-analytic methods to identify differentially active Gene Ontology (GO) terms and gene sets across multiple contrasts.

Key Features:

• Meta-Analytic Integration: mvGST combines one-sided p-values from an input matrix across all genes annotated to each gene set using meta-analytic methods.
• Differential Activity Classification: mvGST classifies gene sets as significantly more active, less active, or not differentially active in each contrast based on combined p-values.
• Profile Assignment: mvGST assigns each gene set to a specific differential-activity profile when multiple contrasts are analyzed.

Scientific Applications:

• Gene set activity analysis: Identification of GO term or gene set activity changes across experimental contrasts in genomics and molecular biology studies.
• Comparative analyses: Comparative assessment of differential activity between conditions such as disease versus healthy or treatment versus control.
• Temporal and multi-condition studies: Profiling gene-set activity patterns across different time points or multiple experimental contrasts.

Methodology:

Combine one-sided p-values from a matrix across genes annotated to each gene set using meta-analytic methods, classify gene sets as more active/less active/not differentially active per contrast, and assign gene-set differential-activity profiles across multiple contrasts.