Copula Index for Detecting Dependence and Monotonicity between
  Stochastic Signals

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Abstract

This paper introduces a nonparametric copula-based approach for detecting the strength and monotonicity of linear and nonlinear statistical dependence between bivariate continuous, discrete or hybrid random variables and stochastic signals, termed CIM. We show that CIM satisfies the data processing inequality and is consequently a self-equitable metric. Simulation results using synthetic datasets reveal that the CIM compares favorably to other state-of-the-art statistical dependency metrics, including the Maximal Information Coefficient (MIC), Randomized Dependency Coefficient (RDC), distance Correlation (dCor), Copula correlation (Ccor), and Copula Statistic (CoS) in both statistical power and sample size requirements. Simulations using real world data highlight the importance of understanding the monotonicity of the dependence structure.

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Vines--a new graphical model for dependent random variables

Tim Bedford, Roger M Cooke (2002)

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NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference

Pau Bellot, Catharina Olsen, Philippe Salembier … (2015)

Background In 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. Results Our 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. Conclusions The 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 material The online version of this article (doi:10.1186/s12859-015-0728-4) contains supplementary material, which is available to authorized users.