Abstract |
The problem of reconstructing large-scale, gene regulatory networks from gene expression data has garnered considerable attention in bioinformatics over the past decade with the graphical modeling paradigm having emerged as a popular framework for inference. Analysis in a full Bayesian setting is contingent upon the assignment of a so-called structure prior-a probability distribution on networks, encoding a priori biological knowledge either in the form of supplemental data or high-level topological features. A key topological consideration is that a wide range of cellular networks are approximately scale-free, meaning that the fraction, , of nodes in a network with degree is roughly described by a power-law with exponent between and . The standard practice, however, is to utilize a random structure prior, which favors networks with binomially distributed degree distributions. In this paper, we introduce a scale-free structure prior for graphical models based on the formula for the probability of a network under a simple scale-free network model. Unlike the random structure prior, its scale-free counterpart requires a node labeling as a parameter. In order to use this prior for large-scale network inference, we design a novel Metropolis-Hastings sampler for graphical models that includes a node labeling as a state space variable. In a simulation study, we demonstrate that the scale-free structure prior outperforms the random structure prior at recovering scale-free networks while at the same time retains the ability to recover random networks. We then estimate a gene association network from gene expression data taken from a breast cancer tumor study, showing that scale-free structure prior recovers hubs, including the previously unknown hub SLC39A6, which is a zinc transporter that has been implicated with the spread of breast cancer to the lymph nodes. Our analysis of the breast cancer expression data underscores the value of the scale-free structure prior as an instrument to aid in the identification of candidate hub genes with the potential to direct the hypotheses of molecular biologists, and thus drive future experiments.
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Authors | Paul Sheridan, Takeshi Kamimura, Hidetoshi Shimodaira |
Journal | PloS one
(PLoS One)
Vol. 5
Issue 11
Pg. e13580
(Nov 05 2010)
ISSN: 1932-6203 [Electronic] United States |
PMID | 21079769
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Chemical References |
- Cation Transport Proteins
- Neoplasm Proteins
- SLC39A6 protein, human
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Topics |
- Algorithms
- Breast Neoplasms
(genetics, pathology)
- Cation Transport Proteins
(genetics)
- Computer Simulation
- Female
- Gene Expression Profiling
- Gene Regulatory Networks
- Genomics
(methods)
- Humans
- Lymphatic Metastasis
- Models, Genetic
- Neoplasm Proteins
(genetics)
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