Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset.
BACKGROUND
Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional 'noise' introduced by non-informative measurements.


RESULTS
We have developed a novel Bayesian hierarchical model and corresponding computational algorithms for clustering gene expression profiles across diverse experimental conditions and studies that accounts for context-specificity of gene expression patterns. The model is based on the Bayesian infinite mixtures framework and does not require a priori specification of the number of clusters. We demonstrate that explicit modeling of context-specificity results in increased accuracy of the cluster analysis by examining the specificity and sensitivity of clusters in microarray data. We also demonstrate that probabilities of co-expression derived from the posterior distribution of clusterings are valid estimates of statistical significance of created clusters.


BACKGROUND
The open-source package gimm is available at http://eh3.uc.edu/gimm.
Liu
X
X
Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.
Sivaganesan
S
S
Yeung
K Y
KY
Guo
J
J
Bumgarner
R E
RE
Medvedovic
Mario
M
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*