Integrating gene expression and GO classification for PCA by preclustering

Background: Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering. Results: Two microarray datasets are analyzed. First, a selection of genes from a well-known Saccharomyces cerevisiae dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jorn R. de Haan, Ester Piek, René C. van Schaik, Jacob de Vlieg, Susanne Bauerschmidt, Lutgarde M. C. Buydens, Ron Wehrens
• Keywords: BMCBI 2010, Gene Expression, GO Categories, Profiles