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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

Heat map displays of the hierarchical clustering results. The light (dark) colour represents over-expressed (under-expressed) genes. The clusterings were performed with the Ward method and Euclidian distance with the 50 genes selected with Random Forest. Genes are displayed in lines and individuals in columns.
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Figure 2: Heat map displays of the hierarchical clustering results. The light (dark) colour represents over-expressed (under-expressed) genes. The clusterings were performed with the Ward method and Euclidian distance with the 50 genes selected with Random Forest. Genes are displayed in lines and individuals in columns.

Mentions: Hierarchical clustering is widely used as a statistical tool for microarray data to look for similarities between genes and samples in an unsupervised way. Hierarchical clustering using the Ward method and Euclidian distance [12] were thus used to evaluate the classification performances of the gene selection. The 50 most important genes were extracted to perform a heatmap (Figure 2). They allowed for a perfect classification of treated vs. control groups, and in each group the 2 genotypes were also clearly separated. Several clusters of genes appeared, e.g. a cluster of genes up regulated in treated animals (bottom), another in LW animals (2nd cluster from bottom).


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

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

Heat map displays of the hierarchical clustering results. The light (dark) colour represents over-expressed (under-expressed) genes. The clusterings were performed with the Ward method and Euclidian distance with the 50 genes selected with Random Forest. Genes are displayed in lines and individuals in columns.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Heat map displays of the hierarchical clustering results. The light (dark) colour represents over-expressed (under-expressed) genes. The clusterings were performed with the Ward method and Euclidian distance with the 50 genes selected with Random Forest. Genes are displayed in lines and individuals in columns.
Mentions: Hierarchical clustering is widely used as a statistical tool for microarray data to look for similarities between genes and samples in an unsupervised way. Hierarchical clustering using the Ward method and Euclidian distance [12] were thus used to evaluate the classification performances of the gene selection. The 50 most important genes were extracted to perform a heatmap (Figure 2). They allowed for a perfect classification of treated vs. control groups, and in each group the 2 genotypes were also clearly separated. Several clusters of genes appeared, e.g. a cluster of genes up regulated in treated animals (bottom), another in LW animals (2nd cluster from bottom).

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