Abstract
BackgroundInferring 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.ResultsIn 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.ConclusionsTIGRESS 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. Moreover, TIGRESS can be run online through the GenePattern platform (GP-DREAM, http://dream.broadinstitute.org).
Highlights
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
We investigate the performance of a popular feature selection method, least angle regression (LARS) [24] combined with stability selection [25,26], for GRN inference
Robust and accurate scoring technique for stability selection, which improves the performance of feature selection with LARS
Summary
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 a notoriously difficult problem, for which the many existing methods reach limited accuracy. Deciphering and understanding TF-TG interactions has many potential far-reaching applications in biology and medicine, ranging from the in silico modeling and simulation of the gene regulatory network (GRN) to the identification of new potential drug targets. Many different approaches have been proposed in the last decade to solve this GRN reverse engineering problem from collections of gene expression data. We refer to [21,22] for detailed reviews and comparisons of existing methods
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