Supplementary material for

Module networks revisited: computational assessment of model predictions

Anagha Joshi, Riet De Smet, Kathleen Marchal, Yves van de Peer and Tom Michoel


The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et. al.(2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.

Supplementary Figures

Yeast data:
Regulator scores for SCPR solution : yeast_segal_modules_regulators.txt
Tight clusters: yeast_tight_modules
Regulator scores for TCPR solution : yeast_tight_modules_regulators.txt
All SCPR modules figures: yeast_segal_modules_figures
All TCPR modules figures: yeast_tight_modules_figures

Mouse data:
Regulator scores for SCPR solution : mouse_segal_modules_regulators.txt
Tight clusters: mouse_tight_modules
Regulator scores for TCPR solution : mouse_tight_modules_regulators.txt
All SCPR modules figures: mouse_segal_modules_figures
All TCPR modules figures: mouse_tight_modules_figures

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