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Predicting qualitative phenotypes from microarray data - the Eadgene pig data set.

Robert-Granié C, Lê Cao KA, Sancristobal M - BMC Proc (2009)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: INRA, UR631 Station d'Amélioration Génétique des Animaux, F-31326 Castanet-Tolosan, France. christele.robert-granie@toulouse.inra.fr

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus

Graphical representation of individuals with the two latent variables associated to the X data set. The first axis (first latent variable) separates the two genotypes, while the second opposes the treatments.
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Figure 3: Graphical representation of individuals with the two latent variables associated to the X data set. The first axis (first latent variable) separates the two genotypes, while the second opposes the treatments.

Mentions: Figure 3 displays the representation of the 24 individuals for the 2 dimensions of the 10+10 sPLS analysis and clearly shows a perfect separation between the 4 classes. The first axis separates the 2 genotypes and the second axis the treatment.


Predicting qualitative phenotypes from microarray data - the Eadgene pig data set.

Robert-Granié C, Lê Cao KA, Sancristobal M - BMC Proc (2009)

Graphical representation of individuals with the two latent variables associated to the X data set. The first axis (first latent variable) separates the two genotypes, while the second opposes the treatments.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2712743&req=5

Figure 3: Graphical representation of individuals with the two latent variables associated to the X data set. The first axis (first latent variable) separates the two genotypes, while the second opposes the treatments.
Mentions: Figure 3 displays the representation of the 24 individuals for the 2 dimensions of the 10+10 sPLS analysis and clearly shows a perfect separation between the 4 classes. The first axis separates the 2 genotypes and the second axis the treatment.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: INRA, UR631 Station d'Amélioration Génétique des Animaux, F-31326 Castanet-Tolosan, France. christele.robert-granie@toulouse.inra.fr

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

No MeSH data available.


Related in: MedlinePlus