Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards
| Autor: | Yehia Mechref, Mahlet G. Tadesse, Lewis K. Pannell, Yue Joseph Wang, Cristina Di Poto, Tsung-Heng Tsai, Habtom W. Ressom |
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| Rok vydání: | 2013 |
| Předmět: |
Statistics and Probability
Proteomics Computer science Bayesian probability computer.software_genre Mass spectrometry Biochemistry Mass Spectrometry Glycomics Bayes' theorem Metabolomics Liquid chromatography–mass spectrometry Profiling (information science) Humans Molecular Biology chemistry.chemical_classification Models Statistical Biomolecule Bayes Theorem Reference Standards Omics Original Papers Computer Science Applications Computational Mathematics Computational Theory and Mathematics chemistry Data mining Data pre-processing computer Algorithms Chromatography Liquid |
| Zdroj: | Bioinformatics (Oxford, England). 29(21) |
| ISSN: | 1367-4811 |
| Popis: | 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: ude.nwotegroeg@rwh Supplementary information: Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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