A novel pipeline method for the preprocessing of Mass spectrometry proteomics data

Mass spectrometry based methodologies allow us to analyze complex mixtures of proteins from biological samples in order to identify important proteomic patterns and discover novel disease biomarkers. However proteomics data are high-dimensional and complex and need to undergo a number of preprocessing steps so that they can be further analyzed. This procedure involves various steps that interact in complex ways and is an open research area, as it has been shown that it has a big effect on the final results. In this work we propose a novel pipeline method for the preprocessing of MS-based data. Our proposed strategy addresses some of the major problems when dealing with proteomics data, like miscalibration, noise and redundancy. Our method exploits the advantages of using the mean spectrum, ensuring at the same time higher sensitivity. In order to illustrate the utility of our proposed method we experimented on data coming from samples of patients with bladder cancer or benign disease. The results were quite encouraging, as 31 statistically important peaks were identified, some of which are not detected by existing methods.