Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Aug 11;11(8):e0160169.
doi: 10.1371/journal.pone.0160169. eCollection 2016.

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

Kim-Anh Lê Cao  1 , Mary-Ellen Costello  1 , Vanessa Anne Lakis  1 , François Bartolo  2 , Xin-Yi Chua  3 , Rémi Brazeilles  4 , Pascale Rondeau  4
Affiliations

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

Kim-Anh Lê Cao et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: 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.

Figures

Fig 1
Fig 1. Comparison between multivariate and univariate statistical analysis frameworks for 16S microbiome data.
(A) Multivariate mixMC framework including processing/normalisation, optional repeated measures design, unsupervised and supervised analyses, (B) Univariate framework, including normalisation and optional repeated measures design analysis.
Fig 2
Fig 2. Most diverse data, PCoA sample plots.
Sample plot on the first two coordinates with (a) weighted Unifrac (b) unweighted Unifrac calculated on the filtered OTU count table (based on 1 674 OTU).
Fig 3
Fig 3. Most diverse data, PCA sample plots.
(a) TSS and (b) TSS multilevel OTU log counts, (c) TSS-ILR and (d) TSS-ILR multilevel normalised log counts, (e) CSS and (f) CSS multilevel log counts.
Fig 4
Fig 4. Most diverse TSS+CLR data, sPLS-DA sample, contribution and cladogram plots.
(a) sample plot on the first two components with 95% confidence level ellipse plots, (b) and (c) represent the contribution of each OTU feature selected on the first (10 OTUs) and second component (120 OTUs), with OTU contribution ranked from bottom (important) to top. Colours indicate body site in which the OTU is most abundant. (d) Cladogram generated from the sPLS-DA result using GraphlAn.
Fig 5
Fig 5. Oral data, sPLS-DA sample plot for the different components.
(a) Component 1 vs. Component 2, (b) Component 2 vs Component 3, using 95% confidence ellipses.
Fig 6
Fig 6. Oral data, contribution and cladogram plots of the features selected for each sPLS-DA component.
(a) Component 1, (b) Component 2, (c) Component 3. In (c) only the top 150 OTU are represented. (d) Cladogram generated from the sPLS-DA results for components 1 and 2 using GraphlAn.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Clarridge J.E.: Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clinical microbiology reviews 17(4), 840–862 (2004) 10.1128/CMR.17.4.840-862.2004 - DOI - PMC - PubMed
    1. Huse S.M., Welch D.M., Morrison H.G., Sogin M.L.: Ironing out the wrinkles in the rare biosphere through improved otu clustering. Environmental microbiology 12(7), 1889–1898 (2010) 10.1111/j.1462-2920.2010.02193.x - DOI - PMC - PubMed
    1. Turnbaugh P.J., Bäckhed F., Fulton L., Gordon J.I.: Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell host & microbe 3(4), 213–223 (2008) 10.1016/j.chom.2008.02.015 - DOI - PMC - PubMed
    1. Turnbaugh P.J., Hamady M., Yatsunenko T., Cantarel B.L., Duncan A., Ley, et al.: A core gut microbiome in obese and lean twins. nature 457 (7228), 480–484 (2009) 10.1038/nature07540 - DOI - PMC - PubMed
    1. Duncan S.H., Lobley G., Holtrop G., Ince J., Johnstone A., Louis P., Flint H.: Human colonic microbiota associated with diet, obesity and weight loss. International journal of obesity 32(11), 1720–1724 (2008) 10.1038/ijo.2008.155 - DOI - PubMed

Substances

Grants and funding

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.