Module networks revisited: computational assessment and prioritization of model predictions
arXiv:0901.1544 · doi:10.1093/bioinformatics/btn658
Abstract
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.
8 pages REVTeX, 6 figures
References in corpus (2)
Cited by in corpus (7)
- Integrative multi-omics module network inference with Lemon-Tree
- Learning modular structures from network data and node variables
- Reverse-engineering transcriptional modules from gene expression data
- Learning differential module networks across multiple experimental conditions
- Detection of regulator genes and eQTLs in gene networks
- Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests
- Model-based clustering of multi-tissue gene expression data