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

被引:17
|
作者
Wilson, Nehemiah [1 ]
Zhao, Ni [2 ]
Zhan, Xiang [3 ]
Koh, Hyunwook [4 ]
Fu, Weijia [5 ]
Chen, Jun [6 ]
Li, Hongzhe [7 ]
Wu, Michael C. [8 ]
Plantinga, Anna M. [1 ]
机构
[1] Williams Coll, Dept Math & Stat, Williamstown, MA 01267 USA
[2] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD 21205 USA
[3] Penn State Coll Med, Dept Publ Hlth Sci, Hershey, PA 17033 USA
[4] State Univ New York, Dept Appl Math & Stat, Korea SUNY Korea, Incheon 21985, South Korea
[5] Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98121 USA
[6] Mayo Clin, Dept Hlth Sci Res, Div Biomed Stat & Informat, Rochester, MN 55905 USA
[7] Univ Penn, Perelman Sch Med, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA
[8] Fred Hutchinson Canc Res Ctr, Publ Hlth Sci Div, Biostat & Biomath Program, Seattle, WA 98109 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
D O I
10.1093/bioinformatics/btaa951
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:1595 / 1597
页数:3
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