Increasing stability and interpretability of gene expression signatures

Abstract : 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.
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Contributeur : Anne-Claire Haury <>
Soumis le : lundi 18 janvier 2010 - 19:02:15
Dernière modification le : vendredi 27 octobre 2017 - 17:32:01
Document(s) archivé(s) le : jeudi 17 juin 2010 - 20:47:44


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  • HAL Id : hal-00448395, version 1
  • ARXIV : 1001.3109



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



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