Identification of microbial interaction network: zero-inflated latent Ising model based approach
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METHODOLOGY
Open Access
Identification of microbial interaction network: zero-inflated latent Ising model based approach Jie Zhou1 , Weston D. Viles3 , Boran Lu1 , Zhigang Li4 , Juliette C. Madan2 , Margaret R. Karagas2 , Jiang Gui1 and Anne G. Hoen1,2* *Correspondence: [email protected] 1 Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA 2 Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH, USA Full list of author information is available at the end of the article
Abstract Background: Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the humanassociated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results: To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is (Continued on next page)
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