A phase synchronization clustering algorithm for identifying interesting groups of genes from cell cycle expression data

Background: The previous studies of genome-wide expression patterns show that a certain percentage of genes are cell cycle regulated. The expression data has been analyzed in a number of different ways to identify cell cycle dependent genes. In this study, we pose the hypothesis that cell cycle dependent genes are considered as oscillating systems with a rhythm, i.e. systems producing response signals with period and frequency. Therefore, we are motivated to apply the theory of multivariate phase synchronization for clustering cell cycle specific genome-wide expression data. Results: We propose the strategy to find groups of genes according to the specific biological process by analyzing cell cycle specific gene expression data. To evaluate the propose method, we use the modified Kuramoto model, which is a phase governing equation that provides the long-term dynamics of globally coupled oscillators. With this equation, we simulate two groups of expression signals, and the simulated si...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Chang Sik Kim, Cheol Soo Bae, Hong Joon Tcha
• Keywords: BMCBI 2008, Cell Cycle, Cycle Dependent Genes, Expression Data