Measuring similarities between gene expression profiles through new data transformations

Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is critical to the analysis. In our study, we developed a new measure for clustering the genes when the key factor is the shape of the profile, and when the expression magnitude should also be accounted for in determining the gene relationship. This is achieved by modeling the shape and magnitude parameters separately in a gene expression profile, and then using the estimated shape and magnitude parameters to define a measure in a new feature space. Results: We explored several different transformation schemes to construct the feature spaces that include a space whose features are determined by the mutual differences of the original expression components, a space derived from a parametric covariance matrix, and the principal component space in traditional PCA analysis. The former two are the newly proposed and the l...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-Beum Lee, Lewis J. Feldman, Haiyan Huang
• Keywords: BMCBI 2007, Expression Profile, Gene Expression, Gene Expression Data