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BMCBI
2008

Microarray data mining using landmark gene-guided clustering

9 years 2 months ago
Microarray data mining using landmark gene-guided clustering
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 biologic...
Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun C
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun Cho, Heenam Stanley Kim, Min-Goo Lee
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