The PLS tool is powerfull to tackle the curse of dimensionality, especially if the dataset suffers form high colinearities or if $n<p$. It even works when the response matrix is multivariate!
That course, given to M2-biostatistics students of ISPED, website introduces that tool and its sparse version thanks to which this is easily possible to select variables.
Recalls over linear regression and non-invertible cases,
PLS1 and PLS2 algorithms.
sPLS from the Lasso point of view.
A case study
Practical work on R
sPLS-DA for single-cell 10X dataset: The application of the PLS method to a Discriminant Analysis case for Single-cell 4 classes discrimination:
sPLS regression: The classical application to the sPLS regression model to the