Module networks revisited: computational assessment and prioritization of model predictions

Details Module networks revisited: computational assessment and prioritization of model predictions Module networks revisited: computational assessment and prioritization of model predictions

2009-01-12

arxiv.org/abs/0901.1544v1

Biology

Quantitative Biology

Quantitative Methods

8 pages REVTeX, 6 figures

Scientific paper

10.1093/bioinformatics/btn658

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.

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