A malignant calibration set was similarly made by taking randomly 15 samples irrespective of whether they belong to stage II or stage III samples. PCA was carried out with each of these calibration www.selleckchem.com/products/PD-0332991.html sets. The PCA scores were used to simulate the profiles of each sample and the sum of squared residuals- ��p[Iop?Isp]2 calculated. Here Iop and Isp are the observed and simulated protein profile intensities, respectively, at point P on the time axis. All samples were now subjected to the Match/No match test using the three parameters, scores of factors, sum of squared residuals, and Mahalanobis distance [16]. The Mahalanobis distance is normally expressed in units of standard deviation.

It is given byD2=(Stest)M?1(Stest)��(1)where Stestis the vector of the scores and sum of squared residuals for a given test sample, and M given by M = ((S��S)/(n ? 1)), where Scontains the corresponding parameters for the calibration set of n standards. To test whether PCA and Discriminant Analysis can be used for objective discrimination between the different stages of malignancy we have also carried out the Match/No Match test with a standard set from Stage III samples alone. 12 samples were randomly selected from the 19 stage III group and PCA was carried out with 6 factors. Though sensitivity and specificity provide a good measure of the diagnostic accuracy, it is to be noted that use of these parameters lead to conflicting demands, since to improve one, the other may have to be sacrificed. Estimating diagnostic accuracy is very important in any kind of diagnostic test, since it gives an idea of how effectively a diagnostic test can differentiate disease from normal condition.

In order to arrive at the best values for sensitivity and specificity, one can apply the technique of Receiver Operating Characteristic (ROC) Curve [17]. We have carried out the estimation of the diagnostic accuracy for both normal and malignant set results by this method. One of the important measures of ROC analysis is finding Area Under the ROC-Curve (AUC), which evaluates the overall performance of the diagnostic test and is considered as the mean value of sensitivity for all the possible values of specificity [18]. The ROC curve analysis illustrates the relationship between the sensitivity and specificity of a diagnostic test. It is a measure of the performance of a diagnostic test. As already pointed out, the opposite trends of sensitivity and specificity make it difficult to arrive at suitable Batimastat threshold/cutoff values for the test parameters.