Estimating sparse gene regulatory networks using a bayesian linear regression.
In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the "net" regulation influence on a gene's expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.
Sarder
Pinaki
P
Department of Electrical and Systems Engineering,Washington University, St. Louis, MO 63130, USA. psarde1@ese.wustl.edu
Schierding
William
W
Cobb
J Perren
JP
Nehorai
Arye
A
D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D007962:Leukocytes / Q000378:metabolism; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D053717:Pneumonia, Ventilator-Associated / Q000235:genetics / Q000378:metabolism; D012984:Software