Abstract
BackgroundElucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions.ResultsIn this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of “true” networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package.ConclusionsOur analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
Highlights
Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology
Survey of differential co-expression methods Numerous methods have been developed for differential co-expression (DC) analysis, mostly over the past decade, and these can be categorised into four broad categories: gene-based, module-based, biclustering, and networkbased methods
Gene-based DC analysis methods identify genes that show changes in associations with other genes across the different conditions. They attempt to quantify the extent to which an individual gene is differentially associated with other genes rather than focusing on the nature, or mechanism, of that differential association. Such genelevel signal could arise from transcription factor (TF) loss of function at the protein level, leading to a loss of regulation across some or all target genes [18]
Summary
Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. While DE methods have been essential to explore differences in the abundance of biomolecules (e.g. RNA), if two targets are simultaneously up- or downregulated, this does not provide any insight as to whether these changes are independent or coordinated This has led to the development of gene-set analysis methods [1,2,3] where genes with a known association are simultaneously tested rather than individual genes; these methods rely on well-defined gene sets. In contrast to exploring DE across conditions, there are opportunities to extract functional information from the co-expression of genes [4] (i.e. concordant changes in transcript abundance) using gene regulatory
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