This case study shows how to use multilevel sPLS-DA to classify treatment groups (LIPO5, GAG+, GAG−, NS) in a repeated-measures HIV vaccine trial dataset. By modelling inter-subject variation, the multilevel framework enhances discrimination of treatment effects while selecting key genes associated with stimulation response. You’ll learn how to explore repeated-measures transcriptomic data, tune model parameters, and visualise treatment-specific gene expression patterns.
🔍 More on sPLS-DA
🔍 More on multilevel methods
📄 Download R script
Data used on this page:vac18
Key functions used on this page:pca()splsda()plotIndiv()cim()
Note:
This case study uses pre-tuned sPLS-DA parameters (optimal.ncomp and optimal.keepX), which were calculated externally and are available for download here.
References:
1. Salmon-Ceron D, Durier C, Desaint C. et al. Immunogenicity and safety of an HIV-1 lipopeptide vaccine in healthy adults: a phase 2 placebo-controlled ANRS trial. AIDS 2010; 24: 2211-23.