MAVTgsa: an R package for gene set (enrichment) analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene set analysis is crucial for understanding coordinated gene expression changes.
• Existing methods lack a systematic tool for identifying diverse gene set significance modules.
• A comprehensive approach is needed to integrate different analytical strategies.

Purpose of the Study:

• To develop and present MAVTgsa, an R package for integrated gene set enrichment analysis.
• To provide a systematic tool for identifying gene set significance modules with various statistical approaches.
• To facilitate the interpretation of gene set enrichment results through visualization.

Main Methods:

• One-sided ordinary least squares (OLS) test for unidirectional gene changes (up- or downregulation).
• Two-sided multivariate analysis of variance (MANOVA) for detecting bidirectional changes across multiple experimental conditions.
• Random forests-based procedure for identifying gene sets predictive of sample conditions or associated with continuous phenotypes.

Main Results:

• MAVTgsa integrates OLS, MANOVA, and random forests for comprehensive gene set analysis.
• The package computes P values and false discovery rate (FDR) q-values for all analyzed gene sets.
• Includes visualization tools to aid in the interpretation of enrichment findings.

Conclusions:

• MAVTgsa offers a versatile and integrated platform for gene set enrichment analysis.
• The package addresses the need for a systematic tool to identify different types of gene set significance modules.
• MAVTgsa enhances the ability to interpret gene expression data in various biological contexts.