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Microarray data mining using landmark gene-guided clustering

Authors
Chopra, PankajKang, JaewooYang, JiongCho, HyungJunKim, Heenam StanleyLee, Min-Goo
Issue Date
11-Feb-2008
Publisher
BMC
Citation
BMC BIOINFORMATICS, v.9
Indexed
SCIE
SCOPUS
Journal Title
BMC BIOINFORMATICS
Volume
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/124085
DOI
10.1186/1471-2105-9-92
ISSN
1471-2105
Abstract
Background: Clustering is a popular data exploration technique widely used in microarray data analysis. Most conventional clustering algorithms, however, generate only one set of clusters independent of the biological context of the analysis. This is often inadequate to explore data from different biological perspectives and gain new insights. We propose a new clustering model that can generate multiple versions of different clusters from a single dataset, each of which highlights a different aspect of the given dataset. Results: By applying our SigCalc algorithm to three yeast Saccharomyces cerevisiae datasets we show two results. First, we show that different sets of clusters can be generated from the same dataset using different sets of landmark genes. Each set of clusters groups genes differently and reveals new biological associations between genes that were not apparent from clustering the original microarray expression data. Second, we show that many of these new found biological associations are common across datasets. These results also provide strong evidence of a link between the choice of landmark genes and the new biological associations found in gene clusters. Conclusion: We have used the SigCalc algorithm to project the microarray data onto a completely new subspace whose co-ordinates are genes (called landmark genes), known to belong to a Biological Process. The projected space is not a true vector space in mathematical terms. However, we use the term subspace to refer to one of virtually infinite numbers of projected spaces that our proposed method can produce. By changing the biological process and thus the landmark genes, we can change this subspace. We have shown how clustering on this subspace reveals new, biologically meaningful clusters which were not evident in the clusters generated by conventional methods. The R scripts (source code) are freely available under the GPL license. The source code is available [see Additional File 1] as additional material, and the latest version can be obtained at http://www4.ncsu.edu/similar to pchopra/landmarks.html. The code is under active development to incorporate new clustering methods and analysis.
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Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
College of Political Science & Economics > Department of Statistics > 1. Journal Articles
College of Health Sciences > School of Biosystems and Biomedical Sciences > 1. Journal Articles
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College of Political Science & Economics (Department of Statistics)
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