TY - GEN
T1 - A novel peak alignment method for LC-MS data analysis using cluster-based techniques
AU - Liu, Yu Cheng
AU - Chen, Lien Chin
AU - Chang, Hui Yin
AU - Wu, Hsin Yi
AU - Liao, Pao Chi
AU - Tseng, S.
PY - 2010
Y1 - 2010
N2 - Recently, liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technique for identifying differential abundance of peaks as biomarkers. Two major problems in the preprocessing of LC-MS data analysis are how to adjust and align multiple LC-MS datasets efficiently and correctly. Hence, an effective algorithm is needed to adjust the variation in retention time and align protein signals automatically. In this study, we proposed a novel algorithm, PeakAlign, based on a clustering technique for adjusting the shifted peaks and aligning the same protein signals from different samples. The PeakAlign algorithm consists of two phases, namely adjustment phase and alignment phase. In the adjustment phase, a LOESS regression method is used to adjust the shifting trend among peaks. In the alignment phase, a cluster-based technique is applied to align the adjusted peaks. For experimental evaluation, two different alignment approaches, SlidingWin algorithm and DTW algorithm, were implemented. Through analyzing the real LC-MS dataset, we demonstrate the usefulness of our proposed algorithm, PeakAlign, on the LC-MS-based samples.
AB - Recently, liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technique for identifying differential abundance of peaks as biomarkers. Two major problems in the preprocessing of LC-MS data analysis are how to adjust and align multiple LC-MS datasets efficiently and correctly. Hence, an effective algorithm is needed to adjust the variation in retention time and align protein signals automatically. In this study, we proposed a novel algorithm, PeakAlign, based on a clustering technique for adjusting the shifted peaks and aligning the same protein signals from different samples. The PeakAlign algorithm consists of two phases, namely adjustment phase and alignment phase. In the adjustment phase, a LOESS regression method is used to adjust the shifting trend among peaks. In the alignment phase, a cluster-based technique is applied to align the adjusted peaks. For experimental evaluation, two different alignment approaches, SlidingWin algorithm and DTW algorithm, were implemented. Through analyzing the real LC-MS dataset, we demonstrate the usefulness of our proposed algorithm, PeakAlign, on the LC-MS-based samples.
UR - http://www.scopus.com/inward/record.url?scp=79952021329&partnerID=8YFLogxK
U2 - 10.1109/BIBMW.2010.5703856
DO - 10.1109/BIBMW.2010.5703856
M3 - Conference contribution
AN - SCOPUS:79952021329
SN - 9781424483044
T3 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
SP - 525
EP - 530
BT - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Y2 - 18 December 2010 through 21 December 2010
ER -