A Bayesian framework for identifying consistent patterns of microbial abundance between body sites
Meier Richard (),
Thompson Jeffrey A.,
Koestler Devin C.,
Michaud Dominique S. and
Kelsey Karl T.
Additional contact information
Koestler Devin C.: Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
Michaud Dominique S.: Department of Public Health and Community Medicine, Tufts University School of Medicine, 136 Harrison Avenue, Boston, MA 02111, USA
Kelsey Karl T.: Department of Epidemiology, Department of Pathology and Laboratory Medicine, Brown University, 70 Ship Street, Providence, RI 02912, USA
Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 6, 15
Recent studies have found that the microbiome in both gut and mouth are associated with diseases of the gut, including cancer. If resident microbes could be found to exhibit consistent patterns between the mouth and gut, disease status could potentially be assessed non-invasively through profiling of oral samples. Currently, there exists no generally applicable method to test for such associations. Here we present a Bayesian framework to identify microbes that exhibit consistent patterns between body sites, with respect to a phenotypic variable. For a given operational taxonomic unit (OTU), a Bayesian regression model is used to obtain Markov-Chain Monte Carlo estimates of abundance among strata, calculate a correlation statistic, and conduct a formal test based on its posterior distribution. Extensive simulation studies demonstrate overall viability of the approach, and provide information on what factors affect its performance. Applying our method to a dataset containing oral and gut microbiome samples from 77 pancreatic cancer patients revealed several OTUs exhibiting consistent patterns between gut and mouth with respect to disease subtype. Our method is well powered for modest sample sizes and moderate strength of association and can be flexibly extended to other research settings using any currently established Bayesian analysis programs.
Keywords: association; Bayesian; consistent pattern; microbial abundance; microbiome; zero-inflated beta regression (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:18:y:2019:i:6:p:15:n:4
Ordering information: This journal article can be ordered from
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().