A powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

TL;DR

This paper introduces T2-DAG, a new statistical test that leverages gene interaction pathway information to more effectively identify differentially expressed gene pathways, outperforming existing methods especially with limited data.

Contribution

The paper develops T2-DAG, a novel Hotelling's $T^2$-type test that incorporates pathway interaction data via a structural equation model for improved detection power.

Findings

T2-DAG controls type-I error well across scenarios.

T2-DAG shows superior power over existing methods.

Application to lung cancer data identifies relevant pathways.

Abstract

A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Besides marginal analyses of individual genes, identification of gene pathways, i.e., a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Due to the typically limited sample size of gene expression datasets, most existing two-sample tests may have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions. We propose T2-DAG, a Hotelling's $T^2$-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions through a linear structural equation model. We establish the asymptotic distribution of the test statistic under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer.