Bayesian alignment model for LC-MS data

Tsung Heng Tsai, Mahlet G. Tadesse, Yue Wang, Habtom W. Ressom

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

Abstract

A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM is composed of two important components: prototype function and mapping function. Estimation of both functions is crucial for the alignment result. We use Markov chain Monte Carlo (MCMC) methods for inference of model parameters. To address the trapping effect of local mode, we propose a block Metropolis-Hastings algorithm that led to better mixing behavior in updating the mapping function coefficients. We applied BAM to both simulated and real LC-MS datasets, and compared its performance with the Bayesian hierarchical curve registration model (BHCR). Performance evaluation on both simulated and real datasets shows satisfactory results in terms of correlation coefficients and ratio of overlapping peak areas.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Pages261-264
Number of pages4
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 - Atlanta, GA, United States
Duration: Nov 12 2011Nov 15 2011

Publication series

NameProceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Other

Other2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Country/TerritoryUnited States
CityAtlanta, GA
Period11/12/1111/15/11

Keywords

  • Bayesian inference
  • Markov chain Monte Carlo (MCMC)
  • block Metropolis-Hastings algorithm
  • liquid chromatography-massspectrometry (LC-MS)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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