mitch

mitch performs multi-contrast gene set enrichment analysis in R to identify coordinated pathway-level activity across multiple contrasts in omics datasets.

Key Features:

• R package implementation: Implemented in R for integration with R-based bioinformatics workflows.
• Multi-Contrast Analysis: Identifies gene sets exhibiting joint enrichment patterns across multiple contrasts or experimental conditions.
• Rank-MANOVA Statistical Approach: Uses a rank-based Multivariate Analysis of Variance (MANOVA) to test for joint enrichment across contrasts.
• Advanced Visualization Capabilities: Generates visualizations that facilitate exploration of enrichment results across up to 20 contrasts simultaneously.
• Versatility Across Omics Applications: Demonstrated on multi-contrast RNA expression profiling, integrative multi-omics analyses, tool benchmarking, and single-cell RNA sequencing.
• Benchmarked Performance: Simulation benchmarking shows comparable accuracy to leading single-contrast tools and improved performance in multi-contrast analyses.

Scientific Applications:

• Clinical and translational research: Exploration of coordinated gene set activity across conditions or time points in clinical omics studies.
• Multi-contrast RNA expression profiling: Analysis of differential expression contrasts across multiple conditions.
• Integrative multi-omics analysis: Pathway-level integration across multiple omics layers.
• Single-cell RNA sequencing: Application to multi-contrast analyses in single-cell RNA-seq experiments.
• Tool benchmarking: Comparative benchmarking of enrichment methods using simulations.

Methodology:

Computational methods explicitly include a rank-based MANOVA to assess joint enrichment across contrasts, simulations for benchmarking performance, and generation of visualizations for up to 20 contrasts.