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Bioinformatics. 2012 Aug 1;28(15):2029-36. doi: 10.1093/bioinformatics/bts312. Epub 2012 Jun 8.

Empirical Bayes conditional independence graphs for regulatory network recovery.

Author information

  • 1Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com

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.

PMID:
22685074
[PubMed - indexed for MEDLINE]
PMCID:
PMC3400959
Free PMC Article

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