A Flexible Quasi-likelihood Model for Microbiome Abundance Count Data

Overview

Journal Stat Med

Publisher Wiley

Specialty Public Health

Date 2023 Aug 22

PMID 37607718

Authors

Yiming Shi

Huilin Li

Chan Wang

Jun Chen

Hongmei Jiang

Ya-Chen T Shih

Haixiang Zhang

Yizhe Song

Yang Feng

Lei Liu

Affiliations

Soon will be listed here.

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.

Citing Articles

Simplified methods for variance estimation in microbiome abundance count data analysis.

Shi Y, Liu L, Chen J, Wylie K, Wylie T, Stout M Front Genet. 2024; 15:1458851.

PMID: 39498319 PMC: 11532193. DOI: 10.3389/fgene.2024.1458851.

Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood.

Liu L, He K, Wang D, Ma S, Qu A, Luan Y Biom J. 2024; 66(6):e202300185.

PMID: 39101657 PMC: 11568107. DOI: 10.1002/bimj.202300185.

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