DNA Microarray Data Analysis

Abstract

Microarrays are one of the latest breakthroughs in experimental molecular biology. It allows monitoring of gene expression for thousands of genes in parallel and providing large amount of valuable data. Clustering analysis is one of the methods to excerpt the contained biological information with arranging genes according to similarity in pattern of gene expression. Since biologists may be difficult to extract the biological information with the raw microarray in mathematics format, the output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. Hierarchical clustering and k-mean clustering is one and the best way to extract the information from the microarrays. In this project, a graphical user interface with a helpful toolbox is provided for the user to analyst the data. Further investigation of the measurement of proximity is also included in this project.