An omnibus test for differential distribution analysis of microbiome sequencing data

Jun Chen, Emily King, Rebecca Deek, Zhi Wei, Yue Yu, DIane Grill, Karla V Ballman

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)643-651
Number of pages9
JournalBioinformatics
Volume34
Issue number4
DOIs
StatePublished - Feb 15 2018

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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