Mining gene expression profiles: an integrated implementation of kernel principal component analysis and singular value decomposition.

ABSTRACT: The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene expression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the inp

SUBMITTER: Reverter F 

PROVIDER: S-EPMC5054124 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature