More power via graph-structured tests for differential expression of gene networks

TL;DR

This paper introduces graph-structured multivariate two-sample tests for detecting differential gene expression, leveraging known gene network structures to improve test power and identify relevant subgraphs in cancer data.

Contribution

It proposes a novel approach that incorporates graph structure into two-sample tests for gene expression, enhancing detection power and subgraph identification capabilities.

Findings

Graph-structured tests outperform traditional methods in simulated data.

Application to cancer datasets reveals biologically relevant gene subgraphs.

Method improves detection of differential expression aligned with gene networks.

Abstract

We consider multivariate two-sample tests of means, where the location shift between the two populations is expected to be related to a known graph structure. An important application of such tests is the detection of differentially expressed genes between two patient populations, as shifts in expression levels are expected to be coherent with the structure of graphs reflecting gene properties such as biological process, molecular function, regulation or metabolism. For a fixed graph of interest, we demonstrate that accounting for graph structure can yield more powerful tests under the assumption of smooth distribution shift on the graph. We also investigate the identification of nonhomogeneous subgraphs of a given large graph, which poses both computational and multiple hypothesis testing problems. The relevance and benefits of the proposed approach are illustrated on synthetic data and on breast and bladder cancer gene expression data analyzed in the context of KEGG and NCI pathways.