A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

Shuler, Kurtis; Verbanic, Samuel; Chen, Irene A.; Lee, Juhee

doi:10.1111/rssc.12493

A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

Journal Article · Wed Jun 23 00:00:00 EDT 2021 · Journal of the Royal Statistical Society, Series C: Applied Statistics

DOI:https://doi.org/10.1111/rssc.12493· OSTI ID:1810768

Shuler, Kurtis ^[1]; Verbanic, Samuel ^[2]; Chen, Irene A. ^[2]; Lee, Juhee ^[3]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of California, Santa Barbara, CA (United States). Chemistry and Biochemistry Dept.
Univ. of California, Santa Cruz, CA (United States). Dept. of Statistics

High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. Here, we develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: National Institutes of Health (NIH); National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1810768

Report Number(s):: SAND--2021-4709J; 695645

Journal Information:: Journal of the Royal Statistical Society, Series C: Applied Statistics, Journal Name: Journal of the Royal Statistical Society, Series C: Applied Statistics Journal Issue: 4 Vol. 70; ISSN 0035-9254

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

References (36)

A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints: LI et al. Li, Qiwei; Guindani, Michele; Reich, Brian J. Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 10, Issue 6 https://doi.org/10.1002/sam.11350	journal	June 2017
Strain- and Species-Level Variation in the Microbiome of Diabetic Wounds Is Associated with Clinical Outcomes and Therapeutic Efficacy Kalan, Lindsay R.; Meisel, Jacquelyn S.; Loesche, Michael A. Cell Host & Microbe, Vol. 25, Issue 5 https://doi.org/10.1016/j.chom.2019.03.006	journal	May 2019
Stick-breaking autoregressive processes Griffin, J. E.; Steel, M. F. J. Journal of Econometrics, Vol. 162, Issue 2 https://doi.org/10.1016/j.jeconom.2011.03.001	journal	June 2011
Temporal Stability in Chronic Wound Microbiota Is Associated With Poor Healing Loesche, Michael; Gardner, Sue E.; Kalan, Lindsay Journal of Investigative Dermatology, Vol. 137, Issue 1 https://doi.org/10.1016/j.jid.2016.08.009	journal	January 2017
Differential abundance analysis for microbial marker-gene surveys Paulson, Joseph N.; Stine, O. Colin; Bravo, Héctor Corrada Nature Methods, Vol. 10, Issue 12 https://doi.org/10.1038/nmeth.2658	journal	September 2013
Microbial predictors of healing and short-term effect of debridement on the microbiome of chronic wounds Verbanic, Samuel; Shen, Yuning; Lee, Juhee npj Biofilms and Microbiomes, Vol. 6, Issue 1 https://doi.org/10.1038/s41522-020-0130-5	journal	May 2020
MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments Grantham, Neal S.; Guan, Yawen; Reich, Brian J. Journal of the American Statistical Association https://doi.org/10.1080/01621459.2019.1626242	journal	July 2019
Bayesian Graphical Compositional Regression for Microbiome Data Mao, Jialiang; Chen, Yuhan; Ma, Li Journal of the American Statistical Association, Vol. 115, Issue 530 https://doi.org/10.1080/01621459.2019.1647212	journal	August 2019
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Robinson, M. D.; McCarthy, D. J.; Smyth, G. K. Bioinformatics, Vol. 26, Issue 1 https://doi.org/10.1093/bioinformatics/btp616	journal	November 2009
A robust approach for identifying differentially abundant features in metagenomic samples Sohn, Michael B.; Du, Ruofei; An, Lingling Bioinformatics, Vol. 31, Issue 14 https://doi.org/10.1093/bioinformatics/btv165	journal	March 2015
A two-part mixed-effects model for analyzing longitudinal microbiome compositional data Chen, Eric Z.; Li, Hongzhe Bioinformatics, Vol. 32, Issue 17 https://doi.org/10.1093/bioinformatics/btw308	journal	May 2016
Generalized Spatial Dirichlet Process Models Duan, J. A.; Guindani, M.; Gelfand, A. E. Biometrika, Vol. 94, Issue 4 https://doi.org/10.1093/biomet/asm071	journal	August 2007
Latent variable modeling for the microbiome Sankaran, Kris; Holmes, Susan P. Biostatistics, Vol. 20, Issue 4 https://doi.org/10.1093/biostatistics/kxy018	journal	June 2018
Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis Tang, Zheng-Zheng; Chen, Guanhua Biostatistics, Vol. 20, Issue 4 https://doi.org/10.1093/biostatistics/kxy025	journal	June 2018
Bayesian variable selection for multivariate zero-inflated models: Application to microbiome count data Lee, Kyu Ha; Coull, Brent A.; Moscicki, Anna-Barbara Biostatistics, Vol. 21, Issue 3 https://doi.org/10.1093/biostatistics/kxy067	journal	December 2018
Bayesian measures of model complexity and fit Spiegelhalter, David J.; Best, Nicola G.; Carlin, Bradley P. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 64, Issue 4 https://doi.org/10.1111/1467-9868.00353	journal	October 2002
Spatial and temporal variability of the human microbiota Parfrey, L. W.; Knight, R. Clinical Microbiology and Infection, Vol. 18 https://doi.org/10.1111/j.1469-0691.2012.03861.x	journal	July 2012
Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors Millar, Russell B. Biometrics, Vol. 65, Issue 3 https://doi.org/10.1111/j.1541-0420.2008.01162.x	journal	January 2009
Bayesian Nonparametric Nonproportional Hazards Survival Modeling De Iorio, Maria; Johnson, Wesley O.; Müller, Peter Biometrics, Vol. 65, Issue 3 https://doi.org/10.1111/j.1541-0420.2008.01166.x	journal	February 2009
A Time-Series DDP for Functional Proteomics Profiles Nieto-Barajas, Luis E.; Müller, Peter; Ji, Yuan Biometrics, Vol. 68, Issue 3 https://doi.org/10.1111/j.1541-0420.2011.01724.x	journal	January 2012
Bayesian Model Choice: Asymptotics and Exact Calculations Gelfand, A. E.; Dey, D. K. Journal of the Royal Statistical Society: Series B (Methodological), Vol. 56, Issue 3 https://doi.org/10.1111/j.2517-6161.1994.tb01996.x	journal	September 1994
Analysis of the chronic wound microbiota of 2,963 patients by 16S rDNA pyrosequencing Wolcott, Randall D.; Hanson, John D.; Rees, Eric J. Wound Repair and Regeneration, Vol. 24, Issue 1 https://doi.org/10.1111/wrr.12370	journal	December 2015
Topographical and Temporal Diversity of the Human Skin Microbiome Grice, E. A.; Kong, H. H.; Conlan, S. Science, Vol. 324, Issue 5931 https://doi.org/10.1126/science.1171700	journal	May 2009
Modelling of zero-inflation improves inference of metagenomic gene count data Jonsson, Viktor; Österlund, Tobias; Nerman, Olle Statistical Methods in Medical Research, Vol. 28, Issue 12 https://doi.org/10.1177/0962280218811354	journal	November 2018
Negative binomial mixed models for analyzing microbiome count data Zhang, Xinyan; Mallick, Himel; Tang, Zaixiang BMC Bioinformatics, Vol. 18, Issue 1 https://doi.org/10.1186/s12859-016-1441-7	journal	January 2017
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Love, Michael I.; Huber, Wolfgang; Anders, Simon Genome Biology, Vol. 15, Issue 12 https://doi.org/10.1186/s13059-014-0550-8	journal	December 2014
Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients Galloway-Peña, Jessica R.; Smith, Daniel P.; Sahasrabhojane, Pranoti Genome Medicine, Vol. 9, Issue 1 https://doi.org/10.1186/s13073-017-0409-1	journal	February 2017
Gibbs Sampling Methods for Stick-Breaking Priors Ishwaran, Hemant; James, Lancelot F. Journal of the American Statistical Association, Vol. 96, Issue 453 https://doi.org/10.1198/016214501750332758	journal	March 2001
An ANOVA Model for Dependent Random Measures De Iorio, Maria; Müller, Peter; Rosner, Gary L. Journal of the American Statistical Association, Vol. 99, Issue 465 https://doi.org/10.1198/016214504000000205	journal	March 2004
Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing Gelfand, Alan E.; Kottas, Athanasios; MacEachern, Steven N. Journal of the American Statistical Association, Vol. 100, Issue 471 https://doi.org/10.1198/016214504000002078	journal	September 2005
Bayesian semiparametric inference for multivariate doubly-interval-censored data Jara, Alejandro; Lesaffre, Emmanuel; De Iorio, Maria The Annals of Applied Statistics, Vol. 4, Issue 4 https://doi.org/10.1214/10-AOAS368	journal	December 2010
Nonparametric Bayesian models through probit stick-breaking processes Dunson, David B.; Rodríguez, Abel Bayesian Analysis, Vol. 6, Issue 1 https://doi.org/10.1214/11-BA605	journal	March 2011
Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis Ren, Boyu; Bacallado, Sergio; Favaro, Stefano The Annals of Applied Statistics, Vol. 14, Issue 1 https://doi.org/10.1214/19-AOAS1295	journal	March 2020
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible McMurdie, Paul J.; Holmes, Susan PLoS Computational Biology, Vol. 10, Issue 4 https://doi.org/10.1371/journal.pcbi.1003531	journal	April 2014
A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data Lee, Juhee; Sison-Mangus, Marilou Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.00522	journal	March 2018
A longitudinal study of the diabetic skin and wound microbiome Gardiner, Melissa; Vicaretti, Mauro; Sparks, Jill PeerJ, Vol. 5 https://doi.org/10.7717/peerj.3543	journal	January 2017

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A Bayesian nonparametric analysis for zero-inflated multivariate count data with application to microbiome study

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