plsgenomics

Statistics
R
Genomics
Supervised methods for dimension reduction in classification and regression framework (in particular PLS-based routines for genomic data analyses).
Authors

Anne-Laure

Boulesteix

Ghislain Durif

Sophie Lambert-Lacroix

Julie Peyre

Korbinian Strimmer

Published

September 15, 2014

(contribution and maintenance, full development since >=1.3 version)

Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.

Programming:

  • R

Keywords:

  • Statistics
  • Supervised learning
  • Dimension reduction
  • Regression
  • Classification
  • High-dimensional data
  • Sparse PLS
  • Genomics
  • RNA-seq

Project:

  • ABS4NGS