TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

By Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona, Jean-Philippe Vert. BMC Systems Biology, 2012

423 citations recorded by the source corpus.

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).

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c32dc9d86a744b320dae77fb38364a03cd3d5f9

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Citations in the corpus

Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “One first resamples the data into several sub-data based on bootstrap, then apples the Lasso to solve these sub-data regression problems, and aggregates the final score for each feature to select more confident features.”
ScholarWorks @ UTRGV ScholarWorks @ UTRGV (methodology): “To evaluate the method, we explore the performance with a simulated data set, that is, generated from random networks with different number of samples and nodes and expected number of edges per node.”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “SAM is compared with the best results reported by the organizers of the challenge: the Trustful Inference of Gene REgulation using Stability Selection (TIGRESS) (Haury et al., 2012), the Context likelihood of relatedness (CLR) (Faith et al.”