Simulated datasets analysed with PLS under discriminant analysis

PLS and sparse PLS

Simulated datasets analysed with PLS under discriminant analysis

PLS and sparse PLS

Overview

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.

Magistral course

  • 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 on simulated datasets: A 4-classes discrimination simulated problem to show the importance of the cross-validation process for supervized problems rather than explained variance

  • sPLS-DA for single-cell 10X dataset: The application of the PLS method to a Discriminant Analysis case for Single-cell 4 classes discrimination:

    • Statement
    • Correction
  • sPLS regression: The classical application to the sPLS regression model to the liver.toxicity dataset:

    • Statement
    • Correction
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Hadrien Lorenzo
PhD student in Biostatistics

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