This case study shows how to use MixMC with sPLS-DA to classify microbial samples by body site (arterial plaque, saliva, stool) using OTU abundance data from the Koren dataset. MixMC is tailored for microbiome data and applies a CLR transformation to handle compositionality. You’ll learn how to tune the number of components and selected features, interpret discriminatory OTUs, and evaluate classification performance across microbial communities.
🔍 More on MixMC
🔍 More on sPLS-DA
📄 Download R script
Data used on this page:Koren.16S
Key functions used on this page:pca()plsda()splsda()
perf()
tune()plotIndiv()plotVar()cim()network()plotLoadings()
References:
1. Koren, O., Knights, D., Gonzalez, A., Waldron, L., Segata, N., Knight, R., Huttenhower, C. and Ley, R.E., 2013. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput Biol, 9(1), p.e1002863
2. 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
3. Lê Cao, K.-A., Boitard, S., Besse, P.: Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC bioinformatics 12(1), 253 (2011)
4. Rohart F, Gautier B, Singh A, Lê Cao K-A (2017). mixOmics: an R package for ‘omics feature selection and multiple data integration.