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. 2013 Mar-Apr;10(2):494-503.
doi: 10.1109/TCBB.2013.25.

Profile-Based LC-MS data alignment--a Bayesian approach

Tsung-Heng Tsai  1 , Mahlet G Tadesse, Yue Wang, Habtom W Ressom
Affiliations

Profile-Based LC-MS data alignment--a Bayesian approach

Tsung-Heng Tsai et al. IEEE/ACM Trans Comput Biol Bioinform. 2013 Mar-Apr.

Abstract

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.

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Figures

Fig. 1
Fig. 1
Profile-based approach is composed of two components: prototype function and mapping functions, which are estimated from the observed data and are used to align the chromatograms in the proposed BAM.
Fig. 2
Fig. 2
An illustrative example showing the functionalities of the prototype function m(t) and the mapping function ui(t). For example, the intensity of the sample at time t = 1 corresponds to the intensity of the prototype function at time ui(1) = 2, which is given by m(2) = 3.
Fig. 3
Fig. 3
Directed acyclic graph of BAM.
Fig. 4
Fig. 4
One realization of simulated data with different noise levels: (a) no noise, (b) SNR 30, (c) SNR 25, and (d) SNR 20. The first replicate run with no time variation applied is highlighted in bold.
Fig. 5
Fig. 5
(a) Base peak chromatograms of the original LC-MS data. (b) Zooms in the RT range 100-250 for the chromatograms in (a).
Fig. 6
Fig. 6
Aligned chromatograms by (a) DTW, (b) CPM, (c) BHCR, and (d) BAM. (e), (f), (g), and (h) zoom in the RT range 100-250 for the chromatograms in (a), (b), (c), and (d), respectively. Misalignments by DTW and BHCR are observed in (e) and (g).
Fig. 7
Fig. 7
(a) Trace plot of the number of knots in the models visited at each MCMC iteration for the chromatogram from the seventh replicate of the second serum aliquot. (b) Box plot of the number of knots visited by the MCMC sampler for each chromatogram.
Fig. 8
Fig. 8
EICs for selected m/z values of 1,047.12 and 1,575.09. For each m/z value, four plots showing the chromatograms of all seven replicates are depicted: chromatograms for aliquots with serum alone (left) and serum with spiked-in peptides (right) are shown before alignment (top) and after alignment by BAM (bottom).
Fig. 9
Fig. 9
Chromatograms in the metabolomic data sets, M1 and M2, before and after alignment by BAM. The inset is a zoomed part in the middle RT range of the chromatograms.
See this image and copyright information in PMC

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