Rank -based methods for statistical analysis of gene expression microarray data

Gene expression microarray data have great potential in helping researchers to understand the biological mechanisms of disease and hence their diagnosis. How to utilize and analyze these large-scale data to extract useful information is the major challenge of bioinformatics field. In this dissertation, we propose a rank-based framework for the statistical analysis of expression microarray data. We first explore the rank-invariant property of various microarray preprocessing methods, then propose a rank-based classifier called Top-scoring Triplet (TST), and finally we present a maximum entropy model of distribution on ranks.