Differential Markov Random Field Analysis with an Application to Detecting Differential Microbial Community Networks
Tony Cai, Hongzhe Li, Jing Ma, and Yin Xia
Microorganisms such as bacteria form complex ecological community networks with various interactions. Diet and other environmental factors can greatly impact the composition and structure of these microbial communities. Differential analysis of microbial community structures aims to elucidate such systematic changes during an adaptive response to changes in environment. In this paper, we propose a flexible Markov random field model for microbial network structure and introduce a hypothesis testing framework for detecting the differences between networks, also known as differential network analysis. Our global test for differential networks is particularly powerful against sparse alternatives. In addition, we develop a multiple testing procedure with false discovery rate control to identify the structure of the differential network. The proposed method is applied to a gut microbiome study on UK twins to evaluate how age affects the microbial community network.