Bayesian Biclustering of Gene Expression Data

DSpace/Manakin Repository

Bayesian Biclustering of Gene Expression Data

Show simple item record

dc.contributor.author Liu, Jun
dc.contributor.author Gu, Jiajun
dc.date.accessioned 2009-03-27T19:25:52Z
dc.date.issued 2008
dc.identifier.citation Gu, Jiajun and Jun S. Lee. 2008. Bayesian biclustering of gene expression data. BMC Genomics 9(Suppl 1): S4. en
dc.identifier.issn 1471-2164 en
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:2757493
dc.description.abstract Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results: We developed a Bayesian biclustering model (BBC), and implemented a Gibbs sampling procedure for its statistical inference. We showed that Bayesian biclustering model can correctly identify multiple clusters of gene expression data. Using simulated data both from the model and with realistic characters, we demonstrated the BBC algorithm outperforms other methods in both robustness and accuracy. We also showed that the model is stable for two normalization methods, the interquartile range normalization and the smallest quartile range normalization. Applying the BBC algorithm to the yeast expression data, we observed that majority of the biclusters we found are supported by significant biological evidences, such as enrichments of gene functions and transcription factor binding sites in the corresponding promoter sequences. Conclusions: The BBC algorithm is shown to be a robust model-based biclustering method that can discover biologically significant gene-condition clusters in microarray data. The BBC model can easily handle missing data via Monte Carlo imputation and has the potential to be extended to integrated study of gene transcription networks. en
dc.description.sponsorship Statistics en
dc.language.iso en_US en
dc.publisher BioMed Central en
dc.relation.isversionof http://dx.doi.org/10.1186/1471-2164-9-S1-S4 en
dash.license LAA
dc.title Bayesian Biclustering of Gene Expression Data en
dc.relation.journal BMC Genomics en
dash.depositing.author Liu, Jun

Files in this item

Files Size Format View
Gu_BayesianBiclustering.pdf 408.3Kb PDF View/Open

This item appears in the following Collection(s)

  • FAS Scholarly Articles [7501]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

Show simple item record

 
 

Search DASH


Advanced Search
 
 

Submitters