zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Abstract

Abstract Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.