mixMC is a multivariate framework in mixOmics designed for microbiome data analysis, addressing the challenges of sparse and compositional data. It improves upon traditional methods by handling repeated-measures experiments, multiclass problems, and detecting discriminative features. The framework enhances the sPLS-DA method with CLR transformation and a multilevel decomposition approach, enabling better detection of subtle microbial differences, especially in studies with high inter-subject variability due to repeated sampling across different habitats.
Related case studies:
Under each of the below listed case studies, a link to download the full 16S dataset can be found (each case study uses a subset of this data).
MixMC Non-repeated Koren Bodysite Case Study
Koren Bodysite Dataset
MixMC Repeated HMP Bodysite Case Study
HMP Bodysite Dataset
MixMC MixKernel Tara Ocean Case Study
Tara Ocean Dataset
References:
1. Lê Cao KA, Costello ME, Lakis VA, Bartolo F, Chua XY, et al. (2016) MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities. PLOS ONE 11(8): e0160169
2. Aitchison, J., 1982. The statistical analysis of compositional data. Journal of the Royal Statistical Society. Series B (Methodological), pp.139-177.
3. Filzmoser, P., Hron, K. and Reimann, C., 2009. Principal component analysis for compositional data with outliers. Environmetrics, 20(6), pp.621-632.
4. Liquet, B., Lê Cao, K.A., Hocini, H. and Thiébaut, R., 2012. A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC bioinformatics, 13(1), p.325.
5. Westerhuis, J.A., van Velzen, E.J., Hoefsloot, H.C. and Smilde, A.K., 2010. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics, 6(1), pp.119-128.
6. Wang Y and Lê Cao K-A (2019). Managing Batch Effects in Microbiome Data. Briefings in Bioinformatics
Publications where mixMC has been applied:
1. Kong, G., Cao, K., Judd, L., Li, S., Renoir, T., & Hannan, A. (2020). Microbiome profiling reveals gut dysbiosis in a transgenic mouse model of Huntington’s disease. Neurobiology Of Disease, 135, 104268.
2. Murtaza N, Burke LM, Vlahovich N, Charlesson B, O’Neill HM, Ross ML, Campbell KL, Krause L, Morrison M. Analysis of the Effects of Dietary Pattern on the Oral Microbiome of Elite Endurance Athletes. Nutrients. 2019; 11(3):614
Additional notes:
In collaboration with colleagues from INRA Toulouse, France, the package mixKernel is available on our website. This package allows for the integration of different types of data using kernel models. It internally calls functions from the mixOmics package. An example of its usage can be found in the first case study listed below.
We are working on how to manage batch effects in microbiome studies, see [6] and soon a new multivariate method to correct for batch effects.