A flexible quasi-likelihood model for microbiome abundance count data

Yiming Shi, Huilin Li, Chan Wang, Jun Chen, Hongmei Jiang, Ya Chen T. Shih, Haixiang Zhang, Yizhe Song, Yang Feng, Lei Liu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)4632-4643
Number of pages12
JournalStatistics in medicine
Issue number25
StatePublished - Nov 10 2023


  • heteroscedasticity
  • skewness
  • spline
  • zero-inflation


Dive into the research topics of 'A flexible quasi-likelihood model for microbiome abundance count data'. Together they form a unique fingerprint.

Cite this