Peak tree and peak detection for mass spectrometry data

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 the variations nor flexible enough to revise false detection results. To solve the two problems, we first propose peak tree to reveal the hierarchical relation among peak judgments made on different scales. Different 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 through global width information. Experiment results show that, compared with conventional peak detection methods, our method can better resist the severe variations and provide a more consistent result among different spectra.