The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling
Open accessAbstract
INTRODUCTION: The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner. OBJECTIVES: Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow. METHODS: We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples. RESULTS: , retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance. CONCLUSION: The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
A test of missing completely at random for multivariate data with missing values
Little, R. J. A. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478722 · Cited by 8,963
Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,262
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230
Pluskal, T., Castillo, S., Villar-Briones, A., & Orešič, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-395 · Cited by 3,845
Journal of Statistical Software
Leeuw, J. D. (2009) · WIREs Computational Statistics · doi:10.1002/wics.10 · Cited by 3,599
The Human Serum Metabolome (HUSERMET) Consortium, Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.335 · Cited by 3,294
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McGarrah, R. W., Crown, S. B., Zhang, G.-F., Shah, S. H., & Newgard, C. B. (2018) · Circulation Research · doi:10.1161/circresaha.117.311002 · Cited by 423
Machine Learning Applications for Mass Spectrometry-Based Metabolomics
Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020) · Metabolites · doi:10.3390/metabo10060243 · Cited by 357
Integration of GC–MS and LC–MS for untargeted metabolomics profiling
Zeki, Ö. C., Eylem, C. C., Reçber, T., Kır, S., & Nemutlu, E. (2020) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2020.113509 · Cited by 336
Evaluating and minimizing batch effects in metabolomics
Han, W., & Li, L. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21672 · Cited by 105
Park, S., Lee, S.-H., Park, G., & Choi, J. (2026) · Food Research International · doi:10.1016/j.foodres.2026.118616 · Cited by 3
Metadata
- Authors
- Di Guida, R., Engel, J., Allwood, J. W., Weber, R. J. M., Jones, M. R., Sommer, U., Viant, M. R., & Dunn, W. B.
- Venue
- Metabolomics
- Published
- 2016-04-15
- Volume
- 12
- Issue
- 5
- Pages
- 93-93
- ISSN
- 1573-3882 (ISSN-L)
- 1573-3890
- OpenAlex
- W2338241819
- Cited by
- 371
- References
- 45
- FWCI
- 14.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Di Guida, R., Engel, J., Allwood, J. W., Weber, R. J. M., Jones, M. R., Sommer, U., Viant, M. R., & Dunn, W. B. (2016). Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics. https://doi.org/10.1007/s11306-016-1030-9
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