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

doi:10.1093/bioinformatics/btaa951

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

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-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.

Original language	English (US)
Pages (from-to)	1595-1597
Number of pages	3
Journal	Bioinformatics
Volume	37
Issue number	11
DOIs	https://doi.org/10.1093/bioinformatics/btaa951
State	Published - Jun 1 2021

ASJC Scopus subject areas

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

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10.1093/bioinformatics/btaa951

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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.",

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AU - Zhan, Xiang

AU - Koh, Hyunwook

AU - Fu, Weijia

AU - Chen, Jun

AU - Li, Hongzhe

AU - Wu, Michael C.

AU - Plantinga, Anna M.

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AB - 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.

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

Abstract

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