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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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ADAGE signature analysis: differential expression analysis with data-defined gene sets.

Jie Tan¹, Matthew Huyck^2,3, Dongbo Hu²

¹Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.

BMC Bioinformatics

|November 24, 2017

Summary

This summary is machine-generated.

This study introduces ADAGE signature analysis, a novel method for identifying biological processes using data-derived gene signatures instead of manually curated gene sets. This approach enhances gene set analysis for organisms lacking extensive resources and discovers novel biological pathways.

Keywords:

Data-defined gene set Denoising autoencoder Gene set analysis Pseudomonas Aeruginosa Unsupervised feature extraction

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Traditional gene set analysis relies on manually curated gene sets, limiting its application in organisms with sparse resources.
Feature learning offers a new approach to extract functional gene sets directly from large-scale expression data.

Purpose of the Study:

To develop a novel method for identifying perturbed biological processes using data-defined functional gene signatures.
To address the limitations of manually curated gene sets in gene set enrichment analysis.

Main Methods:

Utilized ADAGE (a neural network-based feature extraction approach) to learn expression signatures from public data.
Identified differentially active signatures under specific experimental conditions.
Implemented the method as an open-source R package (ADAGEpath) and a web server.

Main Results:

Demonstrated that learned signatures represent perturbed biological processes, including uncurated or novel ones.
Applied ADAGE signature analysis to Pseudomonas aeruginosa, identifying a link between the MexT regulon and Anr.
Showed consistent results with Gene Set Enrichment Analysis (GSEA) while also revealing new biological insights.

Conclusions:

ADAGE signature analysis provides a powerful alternative for gene set analysis, particularly for non-model organisms.
The developed R package and web server make this approach accessible to the research community.
This data-driven method expands the scope and accuracy of functional gene set analysis.