An omnibus test for differential distribution analysis of microbiome sequencing data

Document Type

Article

Publication Date

2-15-2018

Abstract

Motivation One objective of human microbiome studies is to identify differentially abundant microbes across biological conditions. Previous statistical methods focus on detecting the shift in the abundance and/or prevalence of the microbes and treat the dispersion (spread of the data) as a nuisance. These methods also assume that the dispersion is the same across conditions, an assumption which may not hold in presence of sample heterogeneity. Moreover, the widespread outliers in the microbiome sequencing data make existing parametric models not overly robust. Therefore, a robust and powerful method that allows covariate-dependent dispersion and addresses outliers is still needed for differential abundance analysis. Results We introduce a novel test for differential distribution analysis of microbiome sequencing data by jointly testing the abundance, prevalence and dispersion. The test is built on a zero-inflated negative binomial regression model and winsorized count data to account for zero-inflation and outliers. Using simulated data and real microbiome sequencing datasets, we show that our test is robust across various biological conditions and overall more powerful than previous methods. Availability and implementation R package is available at https://github.com/jchen1981/MicrobiomeDDA. Contact chen.jun2@mayo.edu or zhiwei@njit.edu Supplementary informationSupplementary dataare available at Bioinformatics online.

Identifier

85042540885 (Scopus)

Publication Title

Bioinformatics

External Full Text Location

https://doi.org/10.1093/bioinformatics/btx650

e-ISSN

14602059

ISSN

13674803

PubMed ID

29040451

First Page

643

Last Page

651

Issue

4

Volume

34

Grant

7Department

Fund Ref

Mayo Clinic

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