A novel peak alignment method for LC-MS data analysis using cluster-based techniques

Yu Cheng Liu, Lien Chin Chen, Hui Yin Chang, Hsin Yi Wu, Pao Chi Liao, Vincent S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Pages525-530
Number of pages6
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, China
Duration: 2010 Dec 182010 Dec 21

Publication series

Name2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010

Other

Other2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
CountryChina
CityHongKong
Period10-12-1810-12-21

Fingerprint

Liquid chromatography
Liquid Chromatography
Mass spectrometry
Mass Spectrometry
Proteins
Biomarkers
Cluster Analysis

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

Cite this

Liu, Y. C., Chen, L. C., Chang, H. Y., Wu, H. Y., Liao, P. C., & Tseng, V. S. (2010). A novel peak alignment method for LC-MS data analysis using cluster-based techniques. In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 (pp. 525-530). [5703856] (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010). https://doi.org/10.1109/BIBMW.2010.5703856
Liu, Yu Cheng ; Chen, Lien Chin ; Chang, Hui Yin ; Wu, Hsin Yi ; Liao, Pao Chi ; Tseng, Vincent S. / A novel peak alignment method for LC-MS data analysis using cluster-based techniques. 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. pp. 525-530 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010).
@inproceedings{f1c2f866107346f6b1d7526f28596afb,
title = "A novel peak alignment method for LC-MS data analysis using cluster-based techniques",
abstract = "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.",
author = "Liu, {Yu Cheng} and Chen, {Lien Chin} and Chang, {Hui Yin} and Wu, {Hsin Yi} and Liao, {Pao Chi} and Tseng, {Vincent S.}",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/BIBMW.2010.5703856",
language = "English",
isbn = "9781424483044",
series = "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010",
pages = "525--530",
booktitle = "2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010",

}

Liu, YC, Chen, LC, Chang, HY, Wu, HY, Liao, PC & Tseng, VS 2010, A novel peak alignment method for LC-MS data analysis using cluster-based techniques. in 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010., 5703856, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, pp. 525-530, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, HongKong, China, 10-12-18. https://doi.org/10.1109/BIBMW.2010.5703856

A novel peak alignment method for LC-MS data analysis using cluster-based techniques. / Liu, Yu Cheng; Chen, Lien Chin; Chang, Hui Yin; Wu, Hsin Yi; Liao, Pao Chi; Tseng, Vincent S.

2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. p. 525-530 5703856 (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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, Vincent S.

PY - 2010/12/1

Y1 - 2010/12/1

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

UR - http://www.scopus.com/inward/citedby.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

ER -

Liu YC, Chen LC, Chang HY, Wu HY, Liao PC, Tseng VS. A novel peak alignment method for LC-MS data analysis using cluster-based techniques. In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. p. 525-530. 5703856. (2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010). https://doi.org/10.1109/BIBMW.2010.5703856