A comprehensive comparison of normalization methods for loading control and variance stabilization of reverse-phase protein array data

Loading control (LC) and variance stabilization of reverse-phase protein array (RPPA) data have been challenging mainly due to the small number of proteins in an experiment and the lack of reliable inherent control markers. In this study, we compare eight diferent normalization methods for LC and variance stabilization. Te invariant marker set concept was frst applied to the normalization of high-throughput gene expression data. A set of “invariant” markers are selected to create a virtual reference sample. Ten all the samples are normalized to the virtual reference. We propose a variant of this method in the context of RPPA data normalization and compare it with seven other normalization methods previously reported in the literature. Te invariant marker set method performs well with respect to LC, variance stabilization and association with the immunohistochemistry/forescence in situ hybridization data for three key markers in breast tumor samples, while the other methods have inferior performance. Te proposed method is a promising approach for improving the quality of RPPA data.