Nehemiah Wilson1, Ni Zhao2, Xiang Zhan3, Hyunwook Koh4, Weijia Fu5, Jun Chen6, Hongzhe Li7, Michael C Wu8, Anna M Plantinga1. 1. Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, USA. 2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. 3. Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA. 4. Department of Applied Mathematics and Statistics, The State University of New York, Korea (SUNY Korea), Incheon 21985, South Korea. 5. Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA. 6. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA. 7. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. 8. Public Health Sciences Division, Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
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.
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.