Exception discovery: A novel method for the identification of differentially expressed proteins

The identification of differentially expressed proteins (DEPs) observed under specific conditions is one of the key issues in proteomics research. There are currently several ways to detect the changes of a specific proteins expression level in two-dimensional electrophoresis (2-DE) gel images such as statistical analysis and graphical visualization. However, it is quite difficult to handle the information of an individual protein manually by these methods due to the large distortions of patterns in 2-DE images. This paper proposes a method of analyzing DEPs for a specific disease. In order to automatically extract meaningful DEPs in a set of 2-DE gel images, we have designed an exception function that is suitable to measure the anomalous change of the expression level of an individual protein. We present the comparison results of the proposed method versus a Wilcoxon paired t-test that is one of the widely used statistical analysis methods. Several experiments are performed to address not only the effectiveness of the exception function but also the fact that these two methods can compensate each other practically.