Systematic gene clustering analysis from differentiated human ES Cells

초록

Since human embryonic stem (hES) cells have extensive self-renewal capacity and potential to differentiate into any cell type, they are used for studying various differentiations. Especially, dopaminergic (DA) neurons derived from hES cells are in the limelight as a tool for Parkinson`s disease. However, because of the biological complexity, their principal mechanisms which lead to differentiate specific cell types have been unsolved for so long. In this respect, a systematic view of the biological processes is needed as an alternative. cDNA microarray is a useful systematic tool for monitoring the expression levels of thousands of genes. To find significant meanings in the high-throughput data, various clustering methods are widely used. In the genetic networks, a genetic module is a functional unit composed of genes that perform a specific activity together upon the regulatory information. Because the existing clustering methods have little consideration for systematic properties of organism, there are limits to reverse-engineering the genetic modules. In this study, we developed a novel clustering algorithm, which makes each cluster have a low input weight to approach to a genetic module of the genetic system. This method was applied to a time-series microarray experiment from hES cells differentiated to DA neurons. As a result, our results show that the proposed clustering method can be a powerful tool to analyze microarray data and useful for understanding differentiation properties.