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PLS regression methods and extended tools with application to –omics data

Du Lundi 14 Mars 2016 à 9h00 au Mardi 15 Mars 2016 à 17h30

Université Catholique de Louvain 

ISBA, CIL and SMCS – Louvain-la-Neuve

 

March 14th and 15th 2016 – 9:00 am to 17:30 pm

 

Short course on 

 

 

PLS regression methods and extended tools with application to –omics data

by Philippe Bastien , L’oréal Research and Innovation, France

 

 

Summary

PLS regression is a regularized robust alternative to multiple linear regression when dealing with multicollinearity. PLSR is very useful in particular in the analysis of wide data tables (p> n).

This course which aims to be pedagogic through many applications in R will provide basic elements useful to the understanding of the methods from a theoretical point of view.

PLS regression will be compared to other regularized alternatives such as principal component regression, ridge regression, Lasso and generalized Lasso. Extensions of PLS regression to discriminant analysis (PLS-DA Barker& Rayens), multiblocs, sparsity (variable selection), nonlinearity using kernels or splines, and generalized linear models will be fully described.

Course outline

          Regression and multicolinearity

          Regularization

          PLS Regression and Factorial methods

          PLS Regression and optimization

          SIMPLS

          NIPALS and missing data

          PLS1/PLS2 regression

          PLS DA

          OPLS

          Sparse PCA / Sparse PLS / Sparse PLS DA

          Other regularized methods: LASSO/ELASTICNET/GROUP LASSO

          Univariate soft thresholding & Cyclical coordinate descent algorithm

          PLS regression with high dimensional and low sample size : PLS Kernel

          Generalized PLS regression (PLSLogistique, PLSPoisson, PLSCox)

          Introduction to multiblocs PLS: Regularized Generalized Canonical Correlation Analysis

          Applications to omics data using R with packages: mixomics, glmnet, spls, rgcca, plsRglm, …

Prerequisite

·         Theoretical and practical knowledge in projection methods like Principal Components Analysis

·         Culture in multivariate statistical analysis, machine learning methods, o-mics methods

·         Good knowledge in R programming

Price

·         Free for academics 

·         300€ for non-academics

·         Number of participants limited. 

Room : C045 – LSBA - 20 voie du roman pays – 1348 Louvain-la-Neuve

Information and registration

http://www.uclouvain.be/627982.html

     edt-stat-actu@uclouvain.be and http://www.uclouvain.be/en-isba.html

Dernière mise à jour par EDT STAT-ACTU Mardi 12 Janvier 2016