RT Journal
A1 Cai, Qingpo
A1 Fu, Yinghao
A1 Lyu, Cheng
A1 Wang, Zihe
A1 Rao, Shun
A1 Alvarez, Jessica A.
A1 Bai, Yun
A1 Kang, Jian
A1 Yu, Tianwei
T1 A new framework for exploratory network mediator analysis in omics data
JF Genome Research 
JO Genome Research 
YR 2024 
FD April 01 
VO 34 
IS 4 
SP 642 
OP 654 
DO 10.1101/gr.278684.123 
UL http://genome.cshlp.org/content/34/4/642.abstract 
AB Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures’ impact on the outcome, facilitating data interpretation.