Mediation effect selection in high-dimensional and compositional microbiome data

Haixiang Zhang, Jun Chen, Yang Feng, Chan Wang, Huilin Li, Lei Liu

Research output: Contribution to journalArticlepeer-review

Abstract

The microbiome plays an important role in human health by mediating the path from environmental exposures to health outcomes. The relative abundances of the high-dimensional microbiome data have an unit-sum restriction, rendering standard statistical methods in the Euclidean space invalid. To address this problem, we use the isometric log-ratio transformations of the relative abundances as the mediator variables. To select significant mediators, we consider a closed testing-based selection procedure with desirable confidence. Simulations are provided to verify the effectiveness of our method. As an illustrative example, we apply the proposed method to study the mediation effects of murine gut microbiome between subtherapeutic antibiotic treatment and body weight gain, and identify Coprobacillus and Adlercreutzia as two significant mediators.

Original languageEnglish (US)
Pages (from-to)885-896
Number of pages12
JournalStatistics in Medicine
Volume40
Issue number4
DOIs
StatePublished - Feb 20 2021

Keywords

  • closed testing
  • compositional microbiome data
  • high-dimensional data
  • isometric log-ratio transformation
  • mediation analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Mediation effect selection in high-dimensional and compositional microbiome data'. Together they form a unique fingerprint.

Cite this