Figure 3.

Performance analysis of methods tested. Performance analysis of RAId_DbS, X! Tandem(v1.0), Mascot(v2.1), OMSSA(v2.0), and SEQUEST(v3.2). Panels (A) and (C) display the results from 6, 734 spectra in profile format, while panels (B) and (D) display the results from 6,592 centroidized spectra obtained from [19]. In panels (A) and (B), typical ROC curves are shown with the number of false positives (FP) plotted along the abscissa, and the number of true positives (TP) plotted along the ordinate. Thus, a curve that is more to the upper-left corner implies better performance. To unveil the information in the region of small number of false positives, usually the region of most interest, we have plotted the abscissa in log-scale. In panels (C) and (D), a different types of ROC curves are shown. Defining the cumulative number of true negatives by TN and the cumulative number of false negative by FN, the ROC cuves in panels (C) and (D) plot "1 – specificity" (FP/(FP + TN)) along the abscissa (also in log-scale), and the sensitivity (TP/(TP + FN)) along the ordinate. For each method tested, the area under curve (AUC) of this type of ROC curves, when both axes are plotted in linear scale, is also shown inside parentheses in the figure legend. All the AUC have an uncertainty about ± 0.005. Note that ROC curves of this type do not reflect the total number of correct hits and methods that report very few negatives may result in a lower specificity and superficially seems inferior. For example, X! Tandem may be victimized when evaluated using this type of ROC curves. Also note that in panel (D) the trend of AUC for Mascot, X! Tandem, and SEQUEST is consistent with previously reported results [14]. For X! Tandem, Mascot, OMSSA, and SEQUEST, the default parameters for each method were used in every search. However, the maximum number of miscleavages is set to 3 uniformly. It is observed that analysis using profile data giving rise to better ROC curves than those of centoidized data. Although this may be due to the fact that the profile data contain more information, it may also be caused by spectral quality and sample concentration variations.