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Identification of functional modules based on transcriptional regulation structure.

Birmelé E, Elati M, Rouveirol C, Ambroise C - BMC Proc (2008)

Bottom Line: We propose to cluster genes by co-regulation rather than by co-expression.Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes.Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratoire Statistique et Génome, UMR CNRS 8071, INRA 1152, Tour Evry 2, F-91000 Evry, France. etienne.birmele@genopole.cnrs.fr

ABSTRACT

Background: Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. Clustering algorithms mostly rely on co-expression of genes, that is group together genes having similar expression profiles.

Results: We propose to cluster genes by co-regulation rather than by co-expression. We therefore present an inference algorithm for detecting co-regulated groups from gene expression data and introduce a method to cluster genes given that inferred regulatory structure. Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes.

Conclusion: We evaluate the methods on the stress response of S. Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.

No MeSH data available.


Comparison of the clustering based on LICORN with existing methods. Figure of the scores obtained for hierarchical clustering into 20, 30, 40 and 50 clusters. The red circles are the scores obtained for the similarity matrix given by LICORN and λ = 0.1. The similarity measures which are compared to are euclidian distance, partial correlation and mutual information.
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Figure 3: Comparison of the clustering based on LICORN with existing methods. Figure of the scores obtained for hierarchical clustering into 20, 30, 40 and 50 clusters. The red circles are the scores obtained for the similarity matrix given by LICORN and λ = 0.1. The similarity measures which are compared to are euclidian distance, partial correlation and mutual information.

Mentions: We have finally validated our method by comparing clustering performances based on other similarity matrices. We therefore have computed from the original expression data matrices of euclidian distance, partial correlation [13] and mutual information [14]. To compare clustering results with the same number of clusters, we used the hierarchical clustering method AGNES [15] to cluster the genes in 20, 30, 40, and 50 groups. Figure 3 shows the scores for those three methods as well as for ours with λ = 0.1. It clearly shows that inferring the regulatory network from LICORN preprocessing improves the score of the clustering and provide more biologically relevant clusters.


Identification of functional modules based on transcriptional regulation structure.

Birmelé E, Elati M, Rouveirol C, Ambroise C - BMC Proc (2008)

Comparison of the clustering based on LICORN with existing methods. Figure of the scores obtained for hierarchical clustering into 20, 30, 40 and 50 clusters. The red circles are the scores obtained for the similarity matrix given by LICORN and λ = 0.1. The similarity measures which are compared to are euclidian distance, partial correlation and mutual information.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654972&req=5

Figure 3: Comparison of the clustering based on LICORN with existing methods. Figure of the scores obtained for hierarchical clustering into 20, 30, 40 and 50 clusters. The red circles are the scores obtained for the similarity matrix given by LICORN and λ = 0.1. The similarity measures which are compared to are euclidian distance, partial correlation and mutual information.
Mentions: We have finally validated our method by comparing clustering performances based on other similarity matrices. We therefore have computed from the original expression data matrices of euclidian distance, partial correlation [13] and mutual information [14]. To compare clustering results with the same number of clusters, we used the hierarchical clustering method AGNES [15] to cluster the genes in 20, 30, 40, and 50 groups. Figure 3 shows the scores for those three methods as well as for ours with λ = 0.1. It clearly shows that inferring the regulatory network from LICORN preprocessing improves the score of the clustering and provide more biologically relevant clusters.

Bottom Line: We propose to cluster genes by co-regulation rather than by co-expression.Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes.Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratoire Statistique et Génome, UMR CNRS 8071, INRA 1152, Tour Evry 2, F-91000 Evry, France. etienne.birmele@genopole.cnrs.fr

ABSTRACT

Background: Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. Clustering algorithms mostly rely on co-expression of genes, that is group together genes having similar expression profiles.

Results: We propose to cluster genes by co-regulation rather than by co-expression. We therefore present an inference algorithm for detecting co-regulated groups from gene expression data and introduce a method to cluster genes given that inferred regulatory structure. Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes.

Conclusion: We evaluate the methods on the stress response of S. Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.

No MeSH data available.