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Open Access Methodology article

TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

Anne-Claire Haury123, Fantine Mordelet4, Paola Vera-Licona123 and Jean-Philippe Vert123*

Author Affiliations

1 Centre for Computational Biology, Mines ParisTech, Fontainebleau, F-77300 France

2 , Institut Curie, Paris, F-75248, France

3 , U900, INSERM, Paris, F-75248, France

4 Department of Computer Science, Duke University, Durham, NC 27708, USA

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BMC Systems Biology 2012, 6:145  doi:10.1186/1752-0509-6-145

Published: 22 November 2012

Abstract

Background

Inferring the structure of gene regulatory networks (GRN) from a collection of gene expression data has many potential applications, from the elucidation of complex biological processes to the identification of potential drug targets. It is however a notoriously difficult problem, for which the many existing methods reach limited accuracy.

Results

In this paper, we formulate GRN inference as a sparse regression problem and investigate the performance of a popular feature selection method, least angle regression (LARS) combined with stability selection, for that purpose. We introduce a novel, robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS. The resulting method, which we call TIGRESS (for Trustful Inference of Gene REgulation with Stability Selection), was ranked among the top GRN inference methods in the DREAM5 gene network inference challenge. In particular, TIGRESS was evaluated to be the best linear regression-based method in the challenge. We investigate in depth the influence of the various parameters of the method, and show that a fine parameter tuning can lead to significant improvements and state-of-the-art performance for GRN inference, in both directed and undirected settings.

Conclusions

TIGRESS reaches state-of-the-art performance on benchmark data, including both in silico and in vivo (E. coli and S. cerevisiae) networks. This study confirms the potential of feature selection techniques for GRN inference. Code and data are available on http://cbio.ensmp.fr/tigress webcite. Moreover, TIGRESS can be run online through the GenePattern platform (GP-DREAM, http://dream.broadinstitute.org webcite).

Keywords:
Gene Regulatory Network inference; Feature selection; Gene expression data; LARS; Stability selection