@article{0192d8d4165849afb2d82477bf22789a,
title = "An adaptive multivariate two-sample test with application to microbiome differential abundance analysis",
abstract = "Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential abundance analysis: the individual-based univariate differential abundance analysis and the group-based multivariate differential abundance analysis. The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. The multivariate analysis evaluates the overall shift in the abundance of microbiome composition between two conditions, which provides useful preliminary differential information for the necessity of followup validation studies. In this paper, we present a novel Adaptive multivariate two-sample test for Microbiome Differential Analysis (AMDA) to examine whether the composition of a taxa-set are different between two conditions. Our simulation studies and real data applications demonstrated that the AMDA test was often more powerful than several competing methods while preserving the correct type I error rate. A free implementation of our AMDA method in R software is available at https://github.com/xyz5074/AMDA.",
keywords = "Adaptive microbiome differential analysis (AMDA), Maximum mean discrepancy (MMD), Multivariate two-sample test, Permutation, Subset testing, Taxa-set",
author = "Kalins Banerjee and Ni Zhao and Arun Srinivasan and Lingzhou Xue and Hicks, {Steven D.} and Middleton, {Frank A.} and Rongling Wu and Xiang Zhan",
note = "Funding Information: The authors would like to thank the Associate Editor and two reviewers for their insightful comments that improved the paper. Funding was provided by Quadrant Biosciences Inc. (Research agreement with SH) and NIH STAR (R41 MH111347). Funding Information: This work was supported by Quadrant Biosciences Inc. (Research agreement with SH), the National Institutes of Health grants R41 MH111347 (FM), P50 DA039838 (LX) and National Science Foundation grant DMS-1811552 (LX). Funding Information: Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, R. E., et al. (2009). A core gut microbiome in obese and lean twins. Nature 457, 480–484. doi: 10.1038/nature07540 Virgin, H. W., and Todd, J. A. (2011). Metagenomics and personalized medicine. Cell 147, 44–56. doi: 10.1016/j.cell.2011.09.009 Wang, J., and Jia, H. (2016). Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14:508. doi: 10.1038/nrmicro.2016.83 Weiss, S., Xu, Z. Z., Peddada, S., Amir, A., Bittinger, K., Gonzalez, A., et al. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27. doi: 10.1186/s40168-017-0237-y Wu, C., Chen, J., Kim, J., and Pan, W. (2016). An adaptive association test for microbiome data. Gen. Med. 8:56. doi: 10.1186/s13073-016-0302-3 Zhan, X., Epstein, M. P., and Ghosh, D. (2015). An adaptive genetic association test using double kernel machines. Stat. Biosci. 7, 262–281. doi: 10.1007/s12561-014-9116-2 Zhan, X., Plantinga, A., Zhao, N., and Wu, M. C. (2017a). A fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics 73, 1453–1463. doi: 10.1111/biom. 12684 Zhan, X., Tong, X., Zhao, N., Maity, A., Wu, M. C., and Chen, J. (2017b). A small-sample multivariate kernel machine test for microbiome association studies. Gen. Epidemiol. 41, 210–220. doi: 10.1002/gepi.22030 Zhan, X., Xue, L., Zheng, H., Plantinga, A., Wu, M. C., Schaid, D. J., et al. (2018). A small-sample kernel association test for correlated data with application to microbiome association studies. Gen. Epidemiol. 42, 772–782. doi: 10.1002/gepi.22160 Zhang, X., Mallick, H., and Yi, N. (2016). Zero-inflated negative binomial regression for differential abundance testing in microbiome studies. J. Bioinform. Genom. 2:1. doi: 10.18454/jbg.2016.2.2.1 Zhang, X., Zhang, D., Jia, H., Feng, Q., Wang, D., Liang, D., et al. (2015). The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 21:895. doi: 10.1038/ nm.3914 Zhao, N., Chen, J., Carroll, I. M., Ringel-Kulka, T., Epstein, M. P., Zhou, H., et al. (2015). Testing in microbiome-profiling studies with mirkat, the microbiome regression-based kernel association test. Am. J. Hum. Gen. 96, 797–807. doi: 10.1016/j.ajhg.2015.04.003 Zhao, N., Zhan, X., Guthrie, K. A., Mitchell, C. M., and Larson, J. (2018). Generalized hotelling{\textquoteright}s test for paired compositional data with application to human microbiome studies. Gen. Epidemiol. 42, 459– 469.doi: 10.1002/gepi.22127 Conflict of Interest Statement: The authors declare that this study received funding from a National Institutes of Mental Health STTR award (R41 MH111347) to Quadrant Biosciences, Inc. Quadrant Biosciences was involved with study design, and data collection for the RNA sequencing results employed in this study{\textquoteright}s secondary data analysis (autism microbiome data). SH and FM serve on the scientific and medical advisory boards of Quadrant Biosciences Inc., and SH is a paid consultant for Quadrant Biosciences Inc. Publisher Copyright: Copyright {\textcopyright} 2019 Banerjee, Zhao, Srinivasan, Xue, Hicks, Middleton, Wu and Zhan.",
year = "2019",
doi = "10.3389/fgene.2019.00350",
language = "English (US)",
volume = "10",
journal = "Frontiers in Genetics",
issn = "1664-8021",
publisher = "Frontiers Media S. A.",
number = "APR",
}