title: Negative binomial mixed models for analyzing microbiome count data.
abstract: BACKGROUND
  Recent advances in next-generation sequencing (NGS) technology enable researchers to collect a large volume of metagenomic sequencing data. These data provide valuable resources for investigating interactions between the microbiome and host environmental/clinical factors. In addition to the well-known properties of microbiome count measurements, for example, varied total sequence reads across samples, over-dispersion and zero-inflation, microbiome studies usually collect samples with hierarchical structures, which introduce correlation among the samples and thus further complicate the analysis and interpretation of microbiome count data.
  
  
  RESULTS
  In this article, we propose negative binomial mixed models (NBMMs) for detecting the association between the microbiome and host environmental/clinical factors for correlated microbiome count data. Although having not dealt with zero-inflation, the proposed mixed-effects models account for correlation among the samples by incorporating random effects into the commonly used fixed-effects negative binomial model, and can efficiently handle over-dispersion and varying total reads. We have developed a flexible and efficient IWLS (Iterative Weighted Least Squares) algorithm to fit the proposed NBMMs by taking advantage of the standard procedure for fitting the linear mixed models.
  
  
  CONCLUSIONS
  We evaluate and demonstrate the proposed method via extensive simulation studies and the application to mouse gut microbiome data. The results show that the proposed method has desirable properties and outperform the previously used methods in terms of both empirical power and Type I error. The method has been incorporated into the freely available R package BhGLM ( http://www.ssg.uab.edu/bhglm/ and http://github.com/abbyyan3/BhGLM ), providing a useful tool for analyzing microbiome data.
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.
authors.forename: Xinyan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.|Program in Medical and Population Genetics, the Broad Institute, Cambridge, MA, 02142, USA.
authors.forename: Himel
authors.initials: H
authors.lastname: Mallick
authors.affiliation: Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
authors.forename: Zaixiang
authors.initials: Z
authors.lastname: Tang
authors.affiliation: Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA.
authors.forename: Xiangqin
authors.initials: X
authors.lastname: Cui
authors.affiliation: Department of Food Science and Technology and Core for Applied Genomics and Ecology, University of Nebraska, Lincoln, NE, 68583, USA.
authors.forename: Andrew K
authors.initials: AK
authors.lastname: Benson
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35294-0022, USA. nyi@uab.edu.
authors.forename: Nengjun
authors.initials: N
authors.lastname: Yi
mesh_terms: D000465:Algorithms; D000818:Animals; D001419:Bacteria / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D020407:Internet; D007422:Intestines / Q000382:microbiology; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D064307:Microbiota; D015233:Models, Statistical; D012336:RNA, Ribosomal, 16S / Q000737:chemistry / Q000378:metabolism; D014584:User-Computer Interface
chemical_list: D012336:RNA, Ribosomal, 16S
keywords: Correlated measures; Count data; Metagenomics; Microbiome; Negative binomial model; Penalized Quasi-likelihood; Random effects