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Source Title:
Statistical Applications in Genetics and Molecular Biology
|
Volume/Issue:
Volume 11: Issue 3

Graph Selection with GGMselect

Christophe Giraud 1 , Sylvie Huet 2 ,  and Nicolas Verzelen 3
  • 1 Ecole Polytechnique
  • 2 Institut National de la Recherche Agronomique
  • 3 Institut National de la Recherche Agronomique
DOI:
https://doi.org/10.1515/1544-6115.1625
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Published online:
10 Feb 2012
PDF

Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study.The procedure is implemented in the R-package GGMselect available on the CRAN.

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Journal + Issues

Statistical Applications in Genetics and Molecular Biology

Volume 11: Issue 3
SAGMB publishes significant research on the application of statistical ideas to problems arising from computational biology. The range of topics includes linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarrary data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies.
Online ISSN:
1544-6115
First published:
01 May 2002
Language:
English
Publisher:
De Gruyter

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