Predicting qualitative phenotypes from microarray data – the Eadgene pig data setReport as inadecuate

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BMC Proceedings

, 3:S13

First Online: 16 July 2009DOI: 10.1186-1753-6561-3-S4-S13

Cite this article as: Robert-Granié, C., Lê Cao, KA. & SanCristobal, M. BMC Proc 2009 3Suppl 4: S13. doi:10.1186-1753-6561-3-S4-S13


BackgroundThe 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.

MethodsWe 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.

ResultsAll 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.

ConclusionWe 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.

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Author: Christèle Robert-Granié - Kim-Anh Lê Cao - Magali  SanCristobal


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