A multilevel approach has been added for cross-over design experiments (up to two cross factors), in collaboration with A/Prof B. Liquet (Universite de Bordeaux, France). This approach takes into account the complex structure of repeated measurements from different assays, where different treatments are applied on the same subjects to highlight the treatment effects within subject separately from the biological variation between subject.
Two different frameworks are proposed:
- a discriminant analysis (method = ‘splsda’) enables the selection of features separating the different treatments
- a integrative analysis (method = ‘spls’) enables the interaction of two matched data sets and the selection of subset of correlated variables (positively or negatively) across the samples. The approach is unsupervised: no prior knowledge about the samples groups is included.
The multilevel function first decomposes the variance in the data sets X (and Y) and applies either sPLS-DA or sPLS on the within-subject deviation. One or two-factor analyses are available for sPLS-DA.
Associated functions include: multilevel.R, tune.multilevel.R, pheatmap.multilevel.R (see examples in methods, graphics and case studies).
This is our first step towards repeated measurements designs.
The package has been updated to version 4.0-1 to implement these methodologies. It now requires the library ‘pheatmap’.