Xiaoyun Huang
Lingling Fu
Ban Huo
Tingting He
Xingpeng Jiang
a. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China;
b. School of Computer, Central China Normal University, Wuhan 430079, China;
c. School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China;
d. Collaborative & Innovative Center for Educational Technology, Central China Normal University, Wuhan 430079, China;
e. National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan 430079, China
Funds: We would like to express our deepest appreciation for all the code and data cited in this paper. We are also grateful to Koh Hyunwook for his approaches, which have inspired our work. We thank the reviewers for their helpful and constructive comments. This work has been supported by the National Natural Science Foundation of China (61872157, 61932008, 61532008) and the Key Research and Development Program of Hubei Province (2020BAB017).
Received Date: 2021-04-30
Accepted Date:2021-08-06
Rev Recd Date:2021-08-06
Publish Date:2021-08-16
Abstract
Abstract
The dysbiosis of microbiome may have negative effects on a host phenotype. The microbes related to the host phenotype are regarded as microbial association signals. Recently, statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals. However, the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels (i.e., sparse, low sparse, non-sparse). Actually, the real association patterns related to different host phenotypes are not unique. Here, we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels, designated as MiATDS. In particular, we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information. We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method. We find that type I error rates can be well-controlled and MiATDS shows superior effciency on the power. By applying to real data analysis, MiATDS displays reliable practicability too. The R package is available at https://github.com/XiaoyunHuang33/MiATDS.Keywords: Microbiome association,
Association test,
Sparsity level,
Phylogenetic relevance,
Phenotype
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