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...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Pankaj Chopra, Jaewoo Kang, Jiong Yang, HyungJun Cho, Heenam Stanley Kim, Min-Goo Lee
• Keywords: Biological, Biological Associations, BMCBI 2008, Landmark Genes