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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data.

Antony Kaspi^1,2, Mark Ziemann³

¹Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia.

|July 1, 2020

Summary

mitch is a new R package for multi-contrast gene set enrichment analysis. It accurately identifies and visualizes biological pathway activity across multiple omics data contrasts.

Keywords:

Bioconductor package Differential expression Gene regulation Gene set enrichment analysis Multi-omics Multivariate statistics Pathway analysis Single-cell profiling

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene set enrichment analysis (GSEA) is crucial for interpreting omics data.
Existing tools lack capabilities for multi-contrast GSEA and visualization.
Growing omics datasets necessitate advanced analytical approaches.

Purpose of the Study:

To develop an R package for multi-contrast gene set enrichment analysis.
To enable visualization of enrichments across numerous experimental conditions.
To provide a versatile tool for modern omics data interpretation.

Main Methods:

Developed 'mitch', an R package for multi-contrast GSEA.
Employs a rank-MANOVA statistical approach for joint enrichment detection.
Incorporates unique visualization features for exploring up to 20 contrasts.

Main Results:

mitch accurately identifies gene sets with joint enrichment across multiple contrasts.
Demonstrated utility in RNA sequencing, multi-omics, and single-cell data.
Achieves comparable accuracy to single-contrast tools and superior accuracy for multi-contrast analysis.

Conclusions:

mitch is a versatile and accurate tool for multi-contrast omics data analysis.
Facilitates rapid identification and visualization of gene set enrichments.
Available via Bioconductor for broad accessibility.