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Inferring multiple graphical structures

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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.

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Authors and Affiliations

  1. CNRS UMR 8071, Statistique et Génome, Université d’Évry Val d’Essonne, 523, Place des Terrasses, 91000, Évry, France

    Julien Chiquet & Christophe Ambroise

  2. CNRS UMR 6599 Heudiasyc, Université de Technologie de Compiègne, BP 529, 60205, Compiègne Cedex, France

    Yves Grandvalet

Authors
  1. Julien Chiquet
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  2. Yves Grandvalet
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  3. Christophe Ambroise
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Correspondence to Julien Chiquet.

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Chiquet, J., Grandvalet, Y. & Ambroise, C. Inferring multiple graphical structures. Stat Comput 21, 537–553 (2011). https://doi.org/10.1007/s11222-010-9191-2

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  • DOI: https://doi.org/10.1007/s11222-010-9191-2

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