Visualizing differentially expressed genes

Identification of significantly differentially expressed genes (marker genes) among sample groups is a central issue in microarray analysis. This identification is important to understand the molecular pathway of diseases. Many statistical methods have been proposed to locate marker genes. These methods depend on a cutoff value for selection. A fight-fisted cutoff may omit some of the important marker genes, whereas a generous threshold increases the number of false positives. Although robust models for identifying marker genes more accurately is an area of intense research, effective tools for the evaluation of results is often ignored in the literature. Despite the robustness of many of these methods, there is always some probability of false positives. In this paper, we propose a novel approach that exploits parallel coordinates to visualize the gene expression patterns so that one can compare the expression level changes of the marker genes between sample groups and determine whether the selected marker genes are valid. Such visualization is useful to measure the validity of the marker gene selection process as well as to fine tune the parameters of a particular method. A prediction method based on the selected marker genes is used to measure the reliability of our process.