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|Title: ||Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization|
|Authors: ||Cheng, Kin-on|
Law, Ngai-fong Bonnie
Liew, Alan Wee-chung
|Subjects: ||Gene expression data|
Parallel coordinate visualization
Coherent evolution model
|Issue Date: ||23-Apr-2008 |
|Publisher: ||BioMed Central|
|Citation: ||BMC Bioinformatics, April 2008, v. 9: 210, (p. 1-28).|
|Abstract: ||Background: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster
visualization and analysis.|
Results: We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify
that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the
proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm.
Conclusion: We have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering
algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result.
In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.
|Rights: ||© 2008 Cheng et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Type: ||Journal/Magazine Article|
|Appears in Collections:||EIE Journal/Magazine Articles|
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