Abstract
BackgroundIn the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.ResultsOur open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.ConclusionsThe benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0728-4) contains supplementary material, which is available to authorized users.
Highlights
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed
Results we present the results of the benchmark with the presented methodology and obtained with version 1.0 of the package
In listing 2 we present the different commands used in the netbenchmark function to generate the previously presented results, note that the random seed could be used to compare a new method on the same data than those used in the present study
Summary
A great number of methods for reconstructing gene regulatory networks from expression data have been proposed. Very few tools and datasets allow to evaluate accurately and reproducibly those methods. We propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. In order to gain a system-level understanding, it is necessary to examine how genes interact on a large-scale level. Genes do not work in isolation; they are connected in highly structured networks. Gene Regulatory Networks (GRNs) represent this set of relationships. Reconstructing gene regulatory networks from expression data is a very difficult problem that has seen a continuously rising interest in the past decade, and presumably this trend will continue in the years to come due to the
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