Dr. Nagarajan is Associate Director and Senior Group Leader in the Genome Institute of
Singapore, and Associate Professor in the Department of Medicine and Department of
Computer Science at the National University of Singapore. His research focuses on developing
cutting edge genome analytic tools and using them to study the role of microbial communities
in human health. His team conducts research at the interface of genetics, computer science and
microbiology, in particular using a systems biology approach to understand host-microbiome-
pathogen interactions in various disease conditions. Dr. Nagarajan received a B.A. in Computer
Science and Mathematics from Ohio Wesleyan University in 2000, and a Ph.D. in Computer
Science from Cornell University in 2006 (Advisor: Prof. Uri Keich). He did his postdoctoral work
in the Center for Bioinformatics and Computational Biology at the University of Maryland
working on problems in genome assembly and metagenomics (Advisor: Prof. Mihai Pop).
Abstract
The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC > 0.85), a task that other methods struggle with (AUC-ROC < 0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.
