An adaptive association test for microbiome data

Chong Wu, Jun Chen, Junghi Kim, Wei Pan

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


There is increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. Although existing methods have identified many associations, a proper choice of a phylogenetic distance is critical for the power of these methods. To assess an overall association between the composition of a microbial community and an outcome of interest, we present a novel multivariate testing method called aMiSPU, that is joint and highly adaptive over all observed taxa and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real-data analyses demonstrated that the aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates. The R package MiSPU is available at and CRAN.

Original languageEnglish (US)
Article number56
JournalGenome medicine
Issue number1
StatePublished - May 19 2016

Bibliographical note

Publisher Copyright:
© 2016 Wu et al.


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