Peak tree and peak detection for mass spectrometry data

Peng Zhang, Houqiang Li, Xiaobo Zhou, Stephen Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages127-136
Number of pages10
DOIs
StatePublished - 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: Dec 17 2007Dec 19 2007

Publication series

NameAIP Conference Proceedings
Volume952
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other2007 International Symposium on Computational Models for Life Sciences, CMLS '07
CountryAustralia
CityGold Coast, QLD
Period12/17/0712/19/07

Keywords

  • Mass spectrometry
  • Peak detection
  • Peak tree
  • Scale space theory
  • Wavelet

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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