Applications of Independent Component Analysis to Microarray Data
Dr Yan Li
Microarray techniques have revolutionized molecular biology research by allowing the parallel measurements of genes. This project aims to investigate the applications of independent component analysis (ICA) to gene microarray data. ICA is a statistical method used to estimate underlying sources from observed data. We apply ICA methods for decomposing microarray data into independent components. Each component represents a gene expression pattern of a putative biological process. Genes that exhibit significant up regulation or down regulation within each component are grouped into clusters, and putative biological meaning is assigned to each component. The ICA performance will be evaluated with other existing techniques.