Reverse Engineering Gene Networks Using Global-Local Shrinkage Rules
| Autor: | Daniel F. Linder, Viral Panchal |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
business.industry Computer science Bayesian probability Gene regulatory network Inference Markov chain Monte Carlo Machine learning computer.software_genre 01 natural sciences 010104 statistics & probability 03 medical and health sciences symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Prior probability symbols Bayesian hierarchical modeling Artificial intelligence 0101 mathematics business computer 030304 developmental biology Gibbs sampling Curse of dimensionality Shrinkage |
| DOI: | 10.1101/709741 |
| Popis: | Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study. |
| Databáze: | OpenAIRE |
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