In order to compare these methods, we considered their effects on improving the results of downstream data analysis, which is the main goal of all normalization and preprocessing methods. More specifically, since the main purpose of kinome microarray experiments is to classify differentially and non-differentially phosphorylated peptides, we used sensitivity, specificity, precision, and accuracy as performance measures for peptide classification. The statistical data analysis indicated the superiority of VSN over Log2 method for the accuracy, sensitivity, and precision performance measures. Although we used a quantitative approach for comparing variance stabilization methods, this result is in accordance with other research in the transcriptional DNA community.