# Preface

Matrix Decomposition for Data Integration lab comes in two flavors, light and detailed version. See a short description on what to except from each and feel free to choose the one you want to work with during the workshop.

### Light version

- Shows how we use
`mixOmics`

package for unsupervised dimensionality reduction, PCA (one data set) and GCCA (multiple data sets). - Shows how we use
`mixOmics`

package DIABLO method for supervised data integration. - Gives minimalist explanation of methods, skipping mathematical foundations. Maybe be good as first read or as a quick guide how to use
`mixOmics`

when already knowing the foundations behind the methods.

### Detailed version

- Introduces unsupervised GCCA method starting from basics by successively building upon PCA method and CCA. Gives mathematical foundations, R code from scratch as well as demonstrates how things can be run via
`mixOmics`

package. - Builds further upon GCCA to introduce PLS and PLS-DA and their explanations to supervised integration. R code from scratch and via
`mixOmics`

. - May take some time to go through and be slightly challenging. Recommended when wanting to understand the methods.