Imperial College London
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DrElenaChekmeneva

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Research Associate - Structural Elucidation
 
 
 
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e.chekmeneva

 
 
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Location

 

Institute of Reproductive and Developmental BiologyHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Climaco:2022:10.1021/acs.analchem.1c03592,
author = {Climaco, Pinto R and Karaman, I and Lewis, MR and Hällqvist, J and Kaluarachchi, M and Graça, G and Chekmeneva, E and Durainayagam, B and Ghanbari, M and Ikram, MA and Zetterberg, H and Griffin, J and Elliott, P and Tzoulaki, I and Dehghan, A and Herrington, D and Ebbels, T},
doi = {10.1021/acs.analchem.1c03592},
journal = {Analytical Chemistry},
pages = {5493--5503},
title = {Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.},
url = {http://dx.doi.org/10.1021/acs.analchem.1c03592},
volume = {94},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
AU  - Climaco,Pinto R
AU  - Karaman,I
AU  - Lewis,MR
AU  - Hällqvist,J
AU  - Kaluarachchi,M
AU  - Graça,G
AU  - Chekmeneva,E
AU  - Durainayagam,B
AU  - Ghanbari,M
AU  - Ikram,MA
AU  - Zetterberg,H
AU  - Griffin,J
AU  - Elliott,P
AU  - Tzoulaki,I
AU  - Dehghan,A
AU  - Herrington,D
AU  - Ebbels,T
DO  - 10.1021/acs.analchem.1c03592
EP  - 5503
PY  - 2022///
SN  - 0003-2700
SP  - 5493
TI  - Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.
T2  - Analytical Chemistry
UR  - http://dx.doi.org/10.1021/acs.analchem.1c03592
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35360896
UR  - https://pubs.acs.org/doi/10.1021/acs.analchem.1c03592
UR  - http://hdl.handle.net/10044/1/96770
VL  - 94
ER  -
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