Network-based multivariate gene-set testing

TL;DR

This paper introduces a novel multivariate gene-set testing method based on network analysis, enabling detection of differences in gene-gene interactions between conditions, which traditional univariate methods cannot identify.

Contribution

It develops a new approach for gene-set analysis that tests for differences in gene networks, addressing a gap in existing univariate methods.

Findings

Validated with simulated data showing accurate network difference detection.

Applied to cancer studies revealing biologically relevant gene interactions.

Demonstrated improved power over traditional univariate GSA methods.

Abstract

The identification of predefined groups of genes ("gene-sets") which are differentially expressed between two conditions ("gene-set analysis", or GSA) is a very popular analysis in bioinformatics. GSA incorporates biological knowledge by aggregating over genes that are believed to be functionally related. This can enhance statistical power over analyses that consider only one gene at a time. However, currently available GSA approaches are all based on univariate two-sample comparison of single genes. This means that they cannot test for differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. This paper proposes a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. Testing hypotheses concerning networks is challenging due the nature of the underlying estimation problem. Our starting point is a recent, general approach for high-dimensional two-sample testing. We refine the approach and show how it can be used to perform multivariate, network-based gene-set testing. We validate the approach in simulated examples and show results using high-throughput data from several studies in cancer biology.