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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Gene Set Analysis: Challenges, Opportunities, and Future Research.

Farhad Maleki¹, Katie Ovens¹, Daniel J Hogan¹

¹Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.

Frontiers in Genetics

|July 23, 2020

Summary

This summary is machine-generated.

This study classifies gene set analysis methods for high-throughput gene expression data. It details their assumptions and requirements, guiding future research in method development and evaluation.

Keywords:

gene expression gene set analysis gene set database gene set enrichment sensitivity specificity

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene set analysis is crucial for interpreting high-throughput gene expression data.
Numerous gene set analysis methods exist, each with unique assumptions and performance characteristics.

Purpose of the Study:

To classify existing gene set analysis methods based on their core components.
To elucidate the underlying assumptions and requirements for each method class.
To offer guidance for future research in developing and evaluating gene set analysis tools.

Main Methods:

Classification of gene set analysis methods by functional components.
Analysis of assumptions and requirements inherent to each identified class.

Main Results:

A structured classification of gene set analysis methodologies.
Detailed descriptions of the operational principles, assumptions, and data requirements for each category.

Conclusions:

A standardized framework for understanding gene set analysis methods is presented.
Identified gaps and future research directions for enhancing gene set analysis tools and their evaluation.