In recent years, mass spectrometry data analysis has become an important protein identifi cation technique. The mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profi les 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 signifi cantly 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.
|Number of pages||15|
|Journal||International Journal of Data Mining and Bioinformatics|
|State||Published - 24 Feb 2014|
- Biomarker discovery
- Mass spectrometry analysis
- Peak alignment