Title: Effective peak alignment for mass spectrometry data analysis using two-phase clustering approach

Authors: Yu-Cheng Liu; Lien-Chin Chen; Chi-Wei Liu; Vincent S. Tseng

Addresses: Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan 701, Taiwan, ROC ' Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, ROC ' Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 701, Taiwan, ROC ' Department of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng-Kung University, Tainan, 701, Taiwan, ROC

Abstract: In recent years, mass spectrometry data analysis has become an important protein identification technique. 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. However, the existing peak alignment methods are sensitive to noise peaks across various mass spectrometry samples. In this paper, we proposed a novel algorithm named Two-Phase 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.

Keywords: biomarker discovery; mass spectrometry data; peak alignment; data analysis; two-phase clustering; biomarkers; bioinformatics; protein identification; data mining.

DOI: 10.1504/IJDMB.2014.057780

International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.1, pp.52 - 66

Received: 18 Feb 2012
Accepted: 02 Mar 2012
Published online: 21 Oct 2014 *

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