A Survey of Statistical Methods for Microbiome Data Analysis

Kevin C. Lutz, Shuang Jiang, Michael L. Neugent, Nicole J. De Nisco, Xiaowei Zhan, Qiwei Li

Research output: Contribution to journalReview articlepeer-review

Abstract

In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.

Original languageEnglish (US)
Article number884810
JournalFrontiers in Applied Mathematics and Statistics
Volume8
DOIs
StatePublished - Jun 14 2022

Keywords

  • differential abundance analysis
  • integrative analysis
  • metagenomics data
  • microbiome data
  • network analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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