Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards

Abstract

MOTIVATION Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT hwr@georgetown.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

DOI: 10.1093/bioinformatics/btt461

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@article{Tsai2013MultiprofileBA, title={Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards}, author={Tsung-Heng Tsai and Mahlet G. Tadesse and Cristina Di Poto and Lewis K. Pannell and Yehia Mechref and Yue Joseph Wang and Habtom W. Ressom}, journal={Bioinformatics}, year={2013}, volume={29 21}, pages={2774-80} }