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Open Access Open Badges Research article

An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS

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

Author Affiliations

1 Department of Biostatistics, 410 West 10th st., Indianapolis, IN 46202, USA

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

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

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BMC Bioinformatics 2013, 14:123  doi:10.1186/1471-2105-14-123

Published: 10 April 2013



Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment.


The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection.


The proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at webcite.