mixOmics supports multilevel analysis to handle repeated measurements and paired data, addressing individual variation that can obscure treatment effects. This method enhances feature selection and classification accuracy in supervised and unsupervised frameworks. The withinVariation() function extracts within-individual variation, improving PCA, PLS, sPLS, and DA models. The multilevel argument can be used directly in model functions, or decomposition can be performed manually. Use this page to learn how to apply multilevel analysis for improved biological insights in complex datasets.
Data used on this page:vac18
Key functions used on this page:withinVariation()
Related case studies:
Case Study: Multilevel Vac18
Case Study: Multilevel Liver Toxicity
References:
1. 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.