MOTIVATION: 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. AVAILABILITY: The open-source package gimm is available at http://eh3.uc.edu/gimm.
MOTIVATION: 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. AVAILABILITY: The open-source package gimm is available at http://eh3.uc.edu/gimm.
Authors: M Primig; R M Williams; E A Winzeler; G G Tevzadze; A R Conway; S Y Hwang; R W Davis; R E Esposito Journal: Nat Genet Date: 2000-12 Impact factor: 38.330
Authors: Tong Ihn Lee; Nicola J Rinaldi; François Robert; Duncan T Odom; Ziv Bar-Joseph; Georg K Gerber; Nancy M Hannett; Christopher T Harbison; Craig M Thompson; Itamar Simon; Julia Zeitlinger; Ezra G Jennings; Heather L Murray; D Benjamin Gordon; Bing Ren; John J Wyrick; Jean-Bosco Tagne; Thomas L Volkert; Ernest Fraenkel; David K Gifford; Richard A Young Journal: Science Date: 2002-10-25 Impact factor: 47.728
Authors: Richard S Savage; Zoubin Ghahramani; Jim E Griffin; Bernard J de la Cruz; David L Wild Journal: Bioinformatics Date: 2010-06-15 Impact factor: 6.937
Authors: M Medvedovic; R Gear; J M Freudenberg; J Schneider; R Bornschein; M Yan; M J Mistry; H Hendrix; S Karyala; D Halbleib; S Heffelfinger; D J Clegg; M W Anderson Journal: Physiol Genomics Date: 2009-04-07 Impact factor: 3.107