Statistics and Computing

, Volume 21, Issue 4, pp 537–553 | Cite as

Inferring multiple graphical structures

  • Julien Chiquet
  • Yves Grandvalet
  • Christophe Ambroise
Article

Abstract

Gaussian Graphical Models provide a convenient framework for representing dependencies between variables. Recently, this tool has received a high interest for the discovery of biological networks. The literature focuses on the case where a single network is inferred from a set of measurements. But, as wetlab data is typically scarce, several assays, where the experimental conditions affect interactions, are usually merged to infer a single network. In this paper, we propose two approaches for estimating multiple related graphs, by rendering the closeness assumption into an empirical prior or group penalties. We provide quantitative results demonstrating the benefits of the proposed approaches. The methods presented in this paper are embeded in the R package simone from version 1.0-0 and later.

Keywords

Network inference Gaussian graphical model Multiple sample setup Cooperative-LASSO Intertwined-LASSO 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Julien Chiquet
    • 1
  • Yves Grandvalet
    • 2
  • Christophe Ambroise
    • 1
  1. 1.CNRS UMR 8071, Statistique et GénomeUniversité d’Évry Val d’EssonneÉvryFrance
  2. 2.CNRS UMR 6599 HeudiasycUniversité de Technologie de CompiègneCompiègne CedexFrance

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