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. 2008;9 Suppl 1(Suppl 1):S4.
doi: 10.1186/1471-2164-9-S1-S4.

Bayesian biclustering of gene expression data

Jiajun Gu  1 , Jun S Liu
Affiliations

Bayesian biclustering of gene expression data

Jiajun Gu et al. BMC Genomics. 2008.

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.

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Figures

Figure 1
Figure 1
Simulated data with two biclusters and the results of the BBC analysis. Bayesian biclustering for simulated datasets. (a) A dataset with two non-overlapping clusters. (b)-(c) The two clusters found by the Bayesian biclustering model from (a). (d) A dataset with two clusters with common genes. (e)-(g) The three clusters found by the Bayesian biclustering model from (d). (h) A dataset with two clusters with both common samples and common genes. (i)-(k) The three clusters found by the Bayesian biclustering model from (h).
Figure 2
Figure 2
Datasets simulated according to the plaid model Datasets for comparison. (a) A dataset with one single cluster (b) A dataset with two clusters, of which both genes and samples overlap.
Figure 3
Figure 3
The Simulated dataset with realistic characters
See this image and copyright information in PMC

References

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