Partial mixture model for tight clustering of gene expression time-course
Yuan, Yinyin, Li, Chang-Tsun and Wilson, Roland. (2008) Partial mixture model for tight clustering of gene expression time-course. BMC Bioinformatics, Vol.9 (No.287). ISSN 1471-2105
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Official URL: http://dx.doi.org/10.1186/1471-2105-9-287
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to
this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.
Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate
information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a
simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms.
Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset
under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion.
|Item Type:||Journal Article|
|Subjects:||Q Science > QR Microbiology|
|Divisions:||Faculty of Science > Computer Science|
|Library of Congress Subject Headings (LCSH):||Gene expression|
|Journal or Publication Title:||BMC Bioinformatics|
|Publisher:||BioMed Central Ltd.|
|Official Date:||18 June 2008|
|Access rights to Published version:||Restricted or Subscription Access|
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