An adaptive association test for microbiome data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-05-19

AUTHORS

Chong Wu, Jun Chen, Junghi Kim, Wei Pan

ABSTRACT

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 https://github.com/ChongWu-Biostat/MiSPU and CRAN. More... »

PAGES

56

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13073-016-0302-3

DOI

http://dx.doi.org/10.1186/s13073-016-0302-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012702447

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27198579


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