Nonnegative Principal Component Analysis for Proteomic Tumor Profiles

Identifying cancer molecular patterns with high accuracy from high-dimensional proteomic pro les presents a challenge for statistical learning and oncology research. In this study, we develop a nonnegative principal component analysis and propose a nonnegative principal component analysis based support vector machine with a sparse coding to conduct e ective feature selection and high-performance proteomic pattern classi cation. We demonstrate the superiority of our algorithm by comparing it with six peer algorithms on four benchmark proteomic tumor pro les under 100 trials of 50% holdout cross validations. We also rigorously show that the overtting problem associated with support vector machines can be overcome by nonnegative principal component analysis with exceptional sensitivities and speci cities. Moreover, we illustrate that nonnegative principal component analysis can be employed to capture meaningful biomarkers.