A flexible quasi-likelihood model for microbiome abundance count data

被引：2

作者：

Shi, Yiming ^{[1
]}

Li, Huilin ^{[2
]}

Wang, Chan ^{[2
]}

Chen, Jun ^{[3
]}

Jiang, Hongmei ^{[4
]}

Shih, Ya-Chen T. ^{[5
]}

Zhang, Haixiang ^{[6
]}

Song, Yizhe ^{[7
]}

Feng, Yang ^{[8
]}

Liu, Lei ^{[1
]}

机构：

[1] Washington Univ St Louis, Div Biostat, St Louis, MO 63130 USA

[2] NYU, Sch Med, Dept Populat Hlth, Div Biostat, New York, NY USA

[3] Mayo Clinic, Div Computat Biol, Rochester, MN USA

[4] Northwestern Univ, Dept Stat, Evanston, IL USA

[5] Univ Texas MD Anderson Canc Ctr, Dept Hlth Serv Res, Houston, TX USA

[6] Tianjin Univ, Ctr Appl Math, Tianjin, Peoples R China

[7] Washington Univ St Louis, Div Biol & Biomed Sci, St. Louis, MO USA

[8] NYU, Coll Global Publ Hlth, Dept Biostat, New York, NY USA

来源：

STATISTICS IN MEDICINE | 2023年 / 42卷 / 25期

关键词：

heteroscedasticity; skewness; spline; zero-inflation; REGRESSION; OBESITY;

D O I：

10.1002/sim.9880

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：4632 / 4643

页数：12

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