Abstract
BackgroundIn recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.ResultsOverall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.ConclusionWe show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.
Highlights
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data
We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods
Implementation differences in LeMoNe versus Genomica As a starting point for the development of LeMoNe, we reimplemented the methodology described by Segal et al [6], which is incorporated in the Genomica software package
Summary
Several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Several studies use expression data, promoter motif data, chromatin immunoprecipitation (ChIP) data and/or prior functional information (e.g. GO classifications [2] or known regulatory network structures) in conjunction to elucidate transcriptional regulatory networks [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] Most of these methods try to unravel the control logic underlying specific expression patterns. Friedman et al pioneered the use of Bayesian networks to learn regulatory networks from expression data [3,4] In these early studies, each gene in the resulting Bayesian network is associated with its individual regulation program, i.e., its own set of parents and conditional probability distribution. As the number of parameters to be estimated in a module network is much smaller than in a full Bayesian network, the currently available gene expression data sets can be large enough for the purpose of learning module networks [6,11,12,19]
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