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Profile-Based LC-MS Data Alignment - A Bayesian Approach
March-April 2013 (vol. 10 no. 2)
pp. 494-503
Tsung-Heng Tsai, Dept. of Electr. & Comput. Eng., Georgetown Univ., Washington, DC, USA
Mahlet G. Tadesse, Dept. of Math. & Stat., Georgetown Univ., Washington, DC, USA
Yue Wang, Dept. of Electr. & Comput. Eng., Virginia Tech, Arlington, VA, USA
Habtom W. Ressom, Dept. of Oncology, Georgetown Univ., Washington, DC, USA
A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets.
Index Terms:
statistical distributions,Bayes methods,biochemistry,bioinformatics,chromatography,inference mechanisms,Markov processes,mass spectroscopic chemical analysis,Monte Carlo methods,proteins,proteomics,Bayesian alignment model,profile-based LC-MS data alignment,feature-based approach,profile-based retention time correction,CPM,continuous profile model,DTW model,dynamic time-warping model,BHCR model,Bayesian hierarchical curve registration model,LC-MS metabolomic data set,LC-MS proteomic data set,SSVS methodology,stochastic search variable selection methodology,mapping function coefficient,posterior distribution,block Metropolis-Hastings algorithm,knot adaptive selection,MCMC sampler,MCMC-based alignment method,inference mechanism,Markov chain Monte Carlo method,prototype function,liquid chromatography-mass spectrometry,Bayes methods,Stochastic processes,Monte Carlo methods,Chromatography-mass spectrometry,stochastic search variable selection (SSVS),Alignment,Bayesian inference,block Metropolis-Hastings algorithm,liquid chromatography-mass spectrometry (LC-MS),Markov chain Monte Carlo (MCMC)
Citation:
Tsung-Heng Tsai, Mahlet G. Tadesse, Yue Wang, Habtom W. Ressom, "Profile-Based LC-MS Data Alignment - A Bayesian Approach," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 494-503, March-April 2013, doi:10.1109/TCBB.2013.25
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