Distance-Correlation based Gene Set Analysis in Longitudinal Studies

TL;DR

This paper introduces dcGSA, a non-parametric, distance-correlation based method for longitudinal gene set analysis that captures complex relationships and accounts for patient heterogeneity, improving detection of disease-related gene sets.

Contribution

The paper presents dcGSA, a novel robust method for longitudinal gene set analysis that detects both linear and nonlinear associations and handles patient heterogeneity.

Findings

dcGSA outperforms existing methods in simulation studies.

dcGSA identifies more disease-related gene sets in real datasets.

dcGSA captures complex gene-outcome relationships.

Abstract

Longitudinal gene expression profiles of patients are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and hence may lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across patients due to the patient heterogeneity, alleviate the confounding effects of some unobserved covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to multiple real datasets of different characteristics, we are able to identify more disease related gene sets than other methods.