title: MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.
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-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.
authors: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
authors: Kim-Anh
authors: http://orcid.org/0000-0003-3923-1116
authors: KA
authors: Lê Cao
authors: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
authors: Mary-Ellen
authors: ME
authors: Costello
authors: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
authors: Vanessa Anne
authors: VA
authors: Lakis
authors: Institut de Mathématiques de Toulouse, UMR CNRS 5219 INSA Université de Toulouse, Toulouse, France.
authors: François
authors: F
authors: Bartolo
authors: Queensland Facility for Advanced Bioinformatics, The Institute for Molecular Bioscience, Brisbane, QLD, Australia.
authors: Xin-Yi
authors: XY
authors: Chua
authors: Danone Nutricia Research, Palaiseau Cedex, France.
authors: Rémi
authors: R
authors: Brazeilles
authors: Danone Nutricia Research, Palaiseau Cedex, France.
authors: Pascale
authors: P
authors: Rondeau
mesh_terms: D000465:Algorithms; D050197:Atherosclerosis / Q000235:genetics / Q000382:microbiology*; D001419:Bacteria / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D064368:Healthy Volunteers; D006801:Humans; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota / Q000235:genetics*; D015233:Models, Statistical; D012336:RNA, Ribosomal, 16S / Q000235:genetics
chemical_list: D012336:RNA, Ribosomal, 16S