An extended linear strategy bridging the gap between regression and SVD decomposition for modeling peptide tandem mass spectrometry data

Tandem mass spectrometry(MS/MS) of peptides is a central technology for proteomics, enabling the identification of thousands of peptides from a complex mixture. However, widely used algorithms are only used for identifying peptide sequences, no method was designed to predict the input levels and the peak intensities of different peptides. In this paper, We have developed a systematic analytical approach based on a family of extended regression models, which permits routine, large scale protein expression profile modeling. Under this common framework, regression problems can be seamlessly transformed to Singular Value Decomposition problem. Thus, gain the robustness and efficiency. This extended regression strategy therefore offers an effective and efficient framework for in-depth investigation of complex mammalian proteomes.