The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data
Abstract
MOTIVATION: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. RESULTS: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.
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Cites
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Cited by
Pang, Z., Lu, Y., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae253 · Cited by 2,291
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
The food metabolome: a window over dietary exposure
Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, J. J., & Wishart, D. S. (2014) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.076133 · Cited by 519
Gorrochategui, E., Jaumot, J., Lacorte, S., & Tauler, R. (2016) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2016.07.004 · Cited by 352
Multilevel data analysis of a crossover designed human nutritional intervention study
van Velzen, E. J. J., Westerhuis, J. A., van Duynhoven, J. P. M., van Dorsten, F. A., Hoefsloot, H. C. J., Jacobs, D. M., Smit, S., Draijer, R., Kroner, C. I., & Smilde, A. K. (2008) · Journal of Proteome Research · doi:10.1021/pr800145j · Cited by 161
Self-citation
Metadata
- Authors
- Smilde, A. K., Jansen, J. J., Hoefsloot, H. C. J., Lamers, R.-J. A. N., van der Greef, J., & Timmerman, M. E.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-05-12
- Volume
- 21
- Issue
- 13
- Pages
- 3043-3048
- ISSN
- 0266-7061 (ISSN-L)
- 1367-4803
- 1460-2059
- OpenAlex
- W2098562143
- Cited by
- 665
- References
- 29
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- closed
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- Smilde, A. K., Jansen, J. J., Hoefsloot, H. C. J., Lamers, R.-J. A. N., van der Greef, J., & Timmerman, M. E. (2005). ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics. https://doi.org/10.1093/bioinformatics/bti476
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