Increasing stability and interpretability of gene expression signatures

Anne-Claire Haury (1, 2) , Laurent Jacob (1, 2) , Jean-Philippe Vert (1, 2)

1 CBIO - Centre de Bioinformatique
2 Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe

Motivation : Molecular signatures for diagnosis or prognosis estimated from large-scale gene expression data often lack robustness and stability, rendering their biological interpretation challenging. Increasing the signature's interpretability and stability across perturbations of a given dataset and, if possible, across datasets, is urgently needed to ease the discovery of important biological processes and, eventually, new drug targets. Results : We propose a new method to construct signatures with increased stability and easier interpretability. The method uses a gene network as side interpretation and enforces a large connectivity among the genes in the signature, leading to signatures typically made of genes clustered in a few subnetworks. It combines the recently proposed graph Lasso procedure with a stability selection procedure. We evaluate its relevance for the estimation of a prognostic signature in breast cancer, and highlight in particular the increase in interpretability and stability of the signature.

Mots clés: signature; model selection; sparsity; network; gene expression

Domaines: Machine Learning [stat.ML]; Applications [stat.AP]; Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Anne-Claire Haury, Laurent Jacob, Jean-Philippe Vert. Increasing stability and interpretability of gene expression signatures. 2010. ⟨hal-00448395⟩