Cancer Informatics

Department of Medicine, Medical Statistics Sectiona, Clinical Pathologyb, Pathology Chair Officec, Nutrition Sciences, Biochemistry & Molecular Biologyd, University of Alabama at Birmingham, Birmingham, Alabama, USA.

Abstract: Background: Most published literature using SELDI-TOF has used traditional techniques in Spectral Analysis such as Fourier transforms and wavelets for denoising. Most of these publications also compare spectra using their most prominent feature, ie, peaks or local maximums. Methods: The maximum intensity value within each window of differentiable m/z values was used to represent the intensity level in that window. We also calculated the ‘Area under the Curve’ (AUC) spanned by each window. Results: Keeping everything else constant, such as pre-processing of the data and the classifier used, the AUC performed much better as a metric of comparison than the peaks in two out of three data sets. In the third data set both metrics performed equivalently. Conclusions: This study shows that the feature used to compare spectra can have an impact on the results of a study attempting to identify biomarkers using SELDI TOF data.