A flexible quasi‐likelihood model for microbiome abundance count data

Author:

Shi Yiming1,Li Huilin2,Wang Chan2,Chen Jun3,Jiang Hongmei4,Shih Ya‐Chen T.5,Zhang Haixiang6ORCID,Song Yizhe7ORCID,Feng Yang8,Liu Lei1ORCID

Affiliation:

1. Division of Biostatistics Washington University in St. Louis St. Louis Missouri USA

2. Division of Biostatistics, Department of Population Health New York University School of Medicine New York New York USA

3. Division of Computational Biology Mayo Clinic Rochester Minnesota USA

4. Department of Statistics Northwestern University Evanston Illinois USA

5. Department of Health Services Research The University of Texas MD Anderson Cancer Center Houston Texas USA

6. Center for Applied Mathematics, Tianjin University Tianjin China

7. Division of Biology and Biomedical Sciences Washington University in St. Louis St. Louis Missouri USA

8. Department of Biostatistics College of Global Public Health, New York University New York New York USA

Abstract

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

Funder

National Center for Advancing Translational Sciences

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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