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Open Access Research article

An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry

Jaesik Jeong1, Xue Shi2, Xiang Zhang2, Seongho Kim3 and Changyu Shen1*

Author Affiliations

1 Department of Biostatistics, Indiana University, 410 West 10th Street, Indianapolis, IN 46202, USA

2 Department of Chemistry, University of Louisville, 2320 South Brook Street, Louisville, KY 40292, USA

3 Department of Bioinformatics and Biostatistics, University of Louisville, 485 E. Gray St, Louisville, KY 40292, USA

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BMC Bioinformatics 2011, 12:392  doi:10.1186/1471-2105-12-392

Published: 10 October 2011

Abstract

Background

Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum.

Results

With a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate.

Conclusions

We developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at http://www.biostat.iupui.edu/~ChangyuShen/index.htm webcite

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