Peak tree: A new tool for multiscale hierarchical representation and peak detection of mass spectrometry data

Peng Zhang, Houqiang Li, Honghui Wang, Wong Stephen, Xiaobo Zhou

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

Original languageEnglish (US)
Article number5072205
Pages (from-to)1054-1066
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - 2011


  • feature selection.
  • Mass spectrometry
  • peak identification
  • peak tree
  • scale-space filtering
  • wavelets

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics
  • Medicine(all)


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