MiRKAT-S: a community-level test of association between the microbiota and survival times View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-02-08

AUTHORS

Anna Plantinga, Xiang Zhan, Ni Zhao, Jun Chen, Robert R. Jenq, Michael C. Wu

ABSTRACT

BackgroundCommunity-level analysis of the human microbiota has culminated in the discovery of relationships between overall shifts in the microbiota and a wide range of diseases and conditions. However, existing work has primarily focused on analysis of relatively simple dichotomous or quantitative outcomes, for example, disease status or biomarker levels. Recently, there is also considerable interest in the relationship between the microbiota and censored survival outcomes, such as in clinical trials. How to conduct community-level analysis with censored survival outcomes is unclear, since standard dissimilarity-based tests cannot accommodate censored survival times and no alternative methods exist.MethodsWe develop a new approach, MiRKAT-S, for community-level analysis of microbiome data with censored survival times. MiRKAT-S uses ecologically informative distance metrics, such as the UniFrac distances, to generate matrices of pairwise distances between individuals’ taxonomic profiles. The distance matrices are transformed into kernel (similarity) matrices, which are used to compare similarity in the microbiota to similarity in survival times between individuals.ResultsSimulation studies using synthetic microbial communities demonstrate correct control of type I error and adequate power. We also apply MiRKAT-S to examine the relationship between the gut microbiota and survival after allogeneic blood or bone marrow transplant.ConclusionsWe present MiRKAT-S, a method that facilitates community-level analysis of the association between the microbiota and survival outcomes and therefore provides a new approach to analysis of microbiome data arising from clinical trials. More... »

PAGES

17

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40168-017-0239-9

DOI

http://dx.doi.org/10.1186/s40168-017-0239-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1083735079

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28179014


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