What is q-values

Automated diagnosis and prognosis of tumors of the central nervous system (CNS) offer overwhelming challenges because of heterogeneous phenotype and genotype behavior of tumor cells (Yang et al. 2003, Pomeroy et al. 2002). Unambiguous characterization of these tumors is essential for accurate prognosis and therapy. Although the present imaging techniques help to explore the anatomical features of brain tumors, they do not provide an effective means of early detection. Currently, the histological examination of brain tumors is widely used for an accurate diagnosis; however, the tumor classification and grading based on histological appearance does not always guarantee absolute accuracy (Yang et al., 2003, Pomeroy et al., 2002). In many cases, it may not be sufficient to detect the detailed changes in the molecular level using a histological examination (Yang et al. 2003) since such examination may not allow accurate prediction of therapeutic responses or prognosis. If the biopsy sample is too small, the problems are aggravated further. Toward achieving a more reliable diagnosis and prognosis of brain tumors, gene expression measures from microarrays are the center of attention to many researchers who are working on tumor prediction schemes. Our proposed tumor prediction scheme is discussed in two chapters in this volume. In part I (this chapter), we use an analysis of variance (ANOVA) model for characterizing the Affymetrix gene expression data from CNS tumor samples (Pomeroy et al. 2002) while in part II we discuss the prediction of tumor classes based on marker genes selected using the techniques developed in this chapter. In this chapter, we estimate the tumor-specific gene expression measures based on the ANOVA model and exploit them to locate the significantly differentially expressed marker genes among different types of tumor samples. We also provide a novel visualization method to validate the marker gene selection process.