This case study shows how to use MixMC with sPLS-DA to classify microbial samples from different body sites (Antecubital fossa, Stool, Subgingival plaque) using OTU data from the Human Microbiome Project (HMP), while accounting for repeated measures from individual subjects. The multilevel framework removes inter-individual variation to improve feature selection and classification. You’ll learn how to tune the model, identify discriminatory OTUs, and interpret body site-specific microbial patterns.
🔍 More on MixMC
🔍 More on MixMC pre-processing
🔍 More on multilevel methods
🔍 More on sPLS-DA
📄 Download R script
Data used on this page:diverse.16S
Key functions used on this page:pca()plsda()splsda()
perf()
tune()plotIndiv()plotVar()cim()plotLoadings()
References:
1. Filzmoser, P., Hron, K. and Reimann, C., 2009. Principal component analysis for compositional data with outliers. Environmetrics, 20(6), pp.621-632.
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. doi: 10.1371/journal.pone.0160169
3. Lê Cao, K.A., Boitard, S. and Besse, P., 2011. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC bioinformatics, 12(1), p.1.
4. Rohart F, Gautier B, Singh A, Lê Cao K-A (2017). mixOmics: an R package for ‘omics feature selection and multiple data integration.