MiRKAT: kernel machine regression-based global association tests for the microbiome

Nehemiah Wilson, Ni Zhao, Xiang Zhan, Hyunwook Koh, Weijia Fu, Jun Chen, Hongzhe Li, Michael C. Wu, Anna M. Plantinga

Research output: Contribution to journalArticlepeer-review

Abstract

SUMMARY: Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. AVAILABILITY AND IMPLEMENTATION: MiRKAT is available on CRAN as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)1595-1597
Number of pages3
JournalBioinformatics (Oxford, England)
Volume37
Issue number11
DOIs
StatePublished - Jul 12 2021

ASJC Scopus subject areas

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

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