Empirical Bayes conditional independence graphs for regulatory network recovery.
| Publication Type | Academic Article |
| Authors | Mahdi R, Madduri A, Wang G, Strulovici-Barel Y, Salit J, Hackett N, Crystal R, Mezey J |
| Journal | Bioinformatics |
| Volume | 28 |
| Issue | 15 |
| Pagination | 2029-36 |
| Date Published | 06/08/2012 |
| ISSN | 1367-4811 |
| Keywords | Algorithms, Bayes Theorem, Gene Expression Profiling, Gene Regulatory Networks |
| Abstract | MOTIVATION: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods. METHODS: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures. RESULTS: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO. We also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion. AVAILABILITY AND IMPLEMENTATION: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx. CONTACT: ramimahdi@yahoo.com or jgm45@cornell.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
| DOI | 10.1093/bioinformatics/bts312 |
| PubMed ID | 22685074 |
| PubMed Central ID | PMC3400959 |