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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...
In quantitative liquid chromatography-mass spectrometry (LC- MS) experiments, variability assessment helps improve experimental design and detect true differences in ion abundance. A peak-level mixed effects model is considered to estimate the variability due to heterogeneity of the biological samples, inconsistency in sample preparation, and instrument variation. We focus on determining the optimal...
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