BMC Genomics 2008, 9:230 http://www.biomedcentral.com/1471-2164/9/230 TFihgruesrheo2ld independent analysis Threshold independent analysis. Each circle represents the area under the ROC curve (AUC) for one array. Labels in the x axis identify the hybridization: RT – R6 (test) with TIGR4 control, GT – G54 with TIGR4 control, RM – R6 with mix control and GM - G54 with mix control. Panel A: Open circles represent the values obtained by analyzing together all the spots in the array. Panel B: For RT and GT hybridizations, open circles represent the AUC values for the set of T, TR and TG spots, filled circles represent the AUC values for the set of R, G and RG spots; for RM and GM hybridizations, open circles represent AUC values for the T, R and G spots, filled circles represent the AUC values for TR, TG and RG spots. control, the spots specific for one strain perform better than the ones that are specific for two strains. Still, the lat- ter have a discriminatory power equivalent to the best per- forming spots in hybridizations with a T control. Threshold dependent analysis We determined the LR threshold that provided the best accuracy in gene presence/absence detection in each of the 16 hybridizations. Figure 3 shows the maximum accuracy values obtained, and the corresponding sensitivities and specificities. Absolute values in the left column should not be directly compared with the values in the right column, as they do not correspond to the same set of spots because the TRG spot set is not included (see methods) when spots are analyzed in classes (right column). In an hypothetical scenario with an unknown strain as test sample, inclusion of the TRG spots would mainly increase true present calls, which would increase the overall accuracy and sensitivity values. Again, we show that using the mix control there is a significant increase in accuracy, either analyzing all spots together (p = 0.001) or in two groups (p = 0.002). In the first case, the improved accuracy is due to a significantly higher specificity (p = 0.002), meaning that the mix con- trol originates less false present calls. On average, using the mix control reduces the number of erroneous calls by 106 per array (99 false present calls and 7 false absent calls), which corresponds to an improvement of specifi- city of 12.5% and does not change significantly the sensi- tivity. When spots are divided in two classes, the lower accuracy associated with the use of the T control is instead due to lower sensitivity. Here, the use of the mix control pro- duces less 68 erroneous calls per array (resulting from an increase of 41 false present calls and a decrease of 110 false absent calls), which corresponds to an improvement of sensitivity of 21% (p = 0.001) and a decrease of 5% in specificity (p = 0.014). Discussion A common argument against the use of the mix control is the increase in the complexity of the data analysis due to a higher number of expected signal classes that need to be analyzed separately [8]. By inspecting Table 1 it was already apparent that even with a single strain control, it is advantageous to analyze separately the spots that recog- nize specifically the control strain from the ones that do not. If such separate analysis were not performed, spot types with correct present and absent calls would be grouped in a class producing the same expected ratio. The improved performance of analyzing the data separated into spot classes, instead of analyzing all spots together, is apparent in Figure 2, where for both types of control AUC values are consistently higher when spots are analyzed Page 5 of 11 (page number not for citation purposes)