Inferring multiple graphical structures
- 421 Downloads
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.
KeywordsNetwork inference Gaussian graphical model Multiple sample setup Cooperative-LASSO Intertwined-LASSO
Unable to display preview. Download preview PDF.
- Bengio, S., Mariéthoz, J., Keller, M.: The expected performance curve. In: ICML Workshop on ROC Analysis in Machine Learning (2005) Google Scholar
- Charbonnier, C., Chiquet, J., Ambroise, C.: Weighted-lasso for structured network inference from time course data. Stat. Appl. Genet. Mol. Biol. 9(1) (2010) Google Scholar
- Efron, B.: The future of indirect evidence. Tech. Rep. 250, Division of Biostatistics. Stanford University (2009) Google Scholar
- Kolar, M.K., Le Song, A.A., Xing, E.P.: Estimating time-varying networks. Ann. Appl. Stat. (2009) Google Scholar
- Rocha, G.V., Zhao, P., Yu, B.: A path following algorithm for sparse pseudo-likelihood inverse covariance estimation (splice) (2008) Google Scholar
- Roth, V., Fischer, B.: The group-lasso for generalized linear models: uniqueness of solutions and efficent algorithms. In: International Conference on Machine Learning (2008) Google Scholar
- Schäfer, J., Strimmer, K.: A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4(1) (2005) Google Scholar
- Villers, F., Schaeffer, B., Bertin, C., Huet, S.: Assessing the validity domains of graphical Gaussian models in order to infer relationships among components of complex biological systems. Stat. Appl. Genet. Mol. Biol. 7(2) (2008) Google Scholar