Affiliations: [a] MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China | [b] Department of Radiology & Bioinformatics Core, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, USA
Abstract: In mass spectrometry (MS) analysis, false peak detection results are unavoidable due to severe spectrum variations. However, most current peak detection methods are neither robust enough to resist spectrum variations nor flexible enough to revise false detection results. To solve these two problems, we first propose peak tree to reveal the hierarchical relation among peak judgments made on different scales. Different peak tree decomposition will lead to different peak detection result, which make it very convenient to revise false result. Then, we propose a closed-loop scheme to iteratively refine peak tree decomposition. Experiment results show that, compared with conventional peak detection methods, our method can better resist spectrum variations and provide a more consistent result among different spectra.
Keywords: Mass spectrometry, peak detection, wavelet, scale space theory, peak tree
