Journal article

MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities

Kim-Anh Le Cao, Mary-Ellen Costello, Vanessa Anne Lakis, Francois Bartolo, Xin-Yi Chua, Remi Brazeilles, Pascale Rondeau

PLOS ONE | PUBLIC LIBRARY SCIENCE | Published : 2016

Abstract

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-..

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University of Melbourne Researchers

Grants

Awarded by National Health and Medical Research Council (NHMRC)


Awarded by Agence Nationale de la Recherche (ANR)


Funding Acknowledgements

KALC was supported in part by the Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute and the National Health and Medical Research Council (NHMRC) Career Development fellowship (APP1087415). FB was supported by the Agence Nationale de la Recherche (ANR) for the SYNTHACS project (ANR-10-BTBR-05-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors confirm that there is no competing interest or financial disclosure to Danone Nutricia Research. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.