Validating module network learning algorithms using simulated data Export

@article{citeulike:1276165, abstract = {BACKGROUND:In 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.RESULTS:Overall, 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.CONCLUSION:We 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.}, author = {Michoel, Tom and Maere, Steven and Bonnet, Eric and Joshi, Anagha and Saeys, Yvan and Van den Bulcke, Tim and Van Leemput, Koenraad and van Remortel, Piet and Kuiper, Martin and Marchal, Kathleen and Van de Peer, Yves}, citeulike-article-id = {1276165}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-8-S2-S5}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17493254}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17493254}, doi = {10.1186/1471-2105-8-S2-S5}, journal = {BMC Bioinformatics}, keywords = {causal-networks}, number = {Suppl 2}, posted-at = {2008-06-16 02:50:39}, priority = {5}, title = {Validating module network learning algorithms using simulated data}, url = {http://dx.doi.org/10.1186/1471-2105-8-S2-S5}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1276165 L3 - citeulike-article-id:1276165 N2 - BACKGROUND:In 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.RESULTS:Overall, 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.CONCLUSION:We 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. IS - Suppl 2 JF - BMC Bioinformatics TI - Validating module network learning algorithms using simulated data VL - 8 KW - causal-networks AU - Michoel, Tom AU - Maere, Steven AU - Bonnet, Eric AU - Joshi, Anagha AU - Saeys, Yvan AU - Van den Bulcke, Tim AU - Van Leemput, Koenraad AU - van Remortel, Piet AU - Kuiper, Martin AU - Marchal, Kathleen AU - Van de Peer, Yves PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-S2-S5 ER -