mixMC

mixMC is a multivariate framework implemented in mixOmics for microbiome data analysis. The framework takes into account the inherent characteristics of microbiome data, namely sparsity (a large number of zeros in the data) and compositionality (occurring naturally in ecosystems, as well as resulting sequencing artefacts). The mixMC framework aims to identify key microbial communities associated with their habitat or environment.

mixMC addresses the limitations of existing multivariate methods for microbiome studies and proposes unique analytical capabilities: it handles compositional and sparse data, repeated-measures experiments and multiclass problems. It also highlights important discriminative features, and provides interpretable graphical outputs to better understand the microbial communities’ contribution to each habitat. The framework from our paper is summarised below:

Functionality of mixMC within mixOmics

mixMC is a pipeline set up for microbial communities, using some of the standards methods in mixOmics but with a bit of tweaking. The method sPLS-DA has been improved with CLR (centered log ratio) transformation and includes a multilevel decomposition for repeated measurements design that are commonly encountered in microbiome studies. The multilevel approach we developed in [4] enables the 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. To account for subject variability, the data variance is decomposed into within variation (due to habitat) and between subject variation [5], similar to a within-subjects ANOVA in univariate analyses.