A two-phase clustering approach for peak alignment in mining mass spectrometry data

In recent years, the mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profiles in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of anal...

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Bibliographic Details
Main Authors: Lien-Chin Chen, Yu-Cheng Liu, Chi-Wei Liu, Tseng, V.S.
Format: Conference Proceeding
Language:English
Subjects:
Biomarker discovery
Biomarkers
Cancer
Clustering
Clustering algorithms
Computer science
Data mining
Filters
Ionization
Mass spectrometry analysis
Mass spectroscopy
Peak alignment
Proteomics
Smoothing methods
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Summary:In recent years, the mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profiles in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of analysis results. In this paper, we proposed a novel algorithm named Two-Phases Clustering for peak Alignment (TPC-Align) to align mass spectrometry peaks across samples in the pre-processing phase. The TPC-Align algorithm sequentially considers the distribution of intensity values and the locations of mass-to-charge ratio values of peaks between samples. Moreover, TPC-Align algorithm can also report a list of significantly differential peaks between samples, which serve as the candidate biomarkers for further biological study. The proposed peak alignment method was compared to the current peak alignment approach based on one-dimension hierarchical clustering through experimental evaluations, and the results show that TPC-Align outperforms the traditional method on the real dataset.
DOI:10.1109/BIBMW.2009.5332099