title: Inference of Environmental Factor-Microbe and Microbe-Microbe Associations from Metagenomic Data Using a Hierarchical Bayesian Statistical Model.
abstract: The inference of associations between environmental factors and microbes and among microbes is critical to interpreting metagenomic data, but compositional bias, indirect associations resulting from common factors, and variance within metagenomic sequencing data limit the discovery of associations. To account for these problems, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints, to estimate absolute microbial abundance and simultaneously infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors. We empirically show the effectiveness of the mLDM model using synthetic data, data from the TARA Oceans project, and a colorectal cancer dataset. Finally, we apply mLDM to 16S sequencing data from the western English Channel and report several associations. Our model can be used on both natural environmental and human metagenomic datasets, promoting the understanding of associations in the microbial community.
authors.affiliation: MOE Key Lab of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Center for Brain Inspired Computing Research (CBICR), TNLIST, Beijing 100084, China; Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China.
authors.forename: Yuqing
authors.initials: Y
authors.lastname: Yang
authors.affiliation: MOE Key Lab of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Center for Brain Inspired Computing Research (CBICR), TNLIST, Beijing 100084, China. Electronic address: ningchen@tsinghua.edu.cn.
authors.forename: Ning
authors.initials: N
authors.lastname: Chen
authors.affiliation: MOE Key Lab of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, Center for Brain Inspired Computing Research (CBICR), TNLIST, Beijing 100084, China; Department of Computer Science and Technology, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China; Program in Computational Biology and Bioinformatics, University of Southern California, CA 90089, USA. Electronic address: tingchen@tsinghua.edu.cn.
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000818:Animals; D001419:Bacteria / Q000145:classification / Q000235:genetics; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D059647:Gene-Environment Interaction; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota / Q000235:genetics*; D015233:Models, Statistical; D012336:RNA, Ribosomal, 16S; D017422:Sequence Analysis, DNA
chemical_list: D012336:RNA, Ribosomal, 16S
keywords: 16S rRNA sequencing; Lognormal-Dirichlet-Multinomial model; OTU-OTU associations; compositional bias; environmental factor-microbe associations; hierarchical Bayesian statistical model; metagenomics; microbe-microbe associations