This article is part of the supplement: EADGENE and SABRE Post-analyses Workshop
Predicting qualitative phenotypes from microarray data – the Eadgene pig data set
1 INRA, UR631 Station d'Amélioration Génétique des Animaux, F-31326 Castanet-Tolosan, France
2 INRA, UMR444 Laboratoire de Génétique Cellulaire, F-31326 Castanet-Tolosan, France
BMC Proceedings 2009, 3(Suppl 4):S13 doi:10.1186/1753-6561-3-S4-S13Published: 16 July 2009
The aim of this work was to study the performances of 2 predictive statistical tools on a data set that was given to all participants of the Eadgene-SABRE Post Analyses Working Group, namely the Pig data set of Hazard et al. (2008). The data consisted of 3686 gene expressions measured on 24 animals partitioned in 2 genotypes and 2 treatments. The objective was to find biomarkers that characterized the genotypes and the treatments in the whole set of genes.
We first considered the Random Forest approach that enables the selection of predictive variables. We then compared the classical Partial Least Squares regression (PLS) with a novel approach called sparse PLS, a variant of PLS that adapts lasso penalization and allows for the selection of a subset of variables.
All methods performed well on this data set. The sparse PLS outperformed the PLS in terms of prediction performance and improved the interpretability of the results.
We recommend the use of machine learning methods such as Random Forest and multivariate methods such as sparse PLS for prediction purposes. Both approaches are well adapted to transcriptomic data where the number of features is much greater than the number of individuals.