Empirical Bayes conditional independence graphs for regulatory network recovery.
BACKGROUND
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.


METHODS
Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx.


BACKGROUND
ramimahdi@yahoo.com or jgm45@cornell.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Genetic Medicine, Weill Cornell Medical College, New York, NY 10065, USA. ramimahdi@yahoo.com
Rami
R
Mahdi
Abishek S
AS
Madduri
Guoqing
G
Wang
Yael
Y
Strulovici-Barel
Jacqueline
J
Salit
Neil R
NR
Hackett
Ronald G
RG
Crystal
Jason G
JG
Mezey
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D020545:Respiratory Mucosa / Q000378:metabolism; D012984:Software