Assessment of PLSDA cross validation

Metabolomics

Volumn 4, Issue 1, 2008, Pages 81-89

  Westerhuis, Johan A ^a   Hoefsloot, Huub C J ^a   Smit, Suzanne ^a   Vis, Daniel J ^a   Smilde, Age K ^a   Velzen, Ewoud J J ^a,b   Duijnhoven, John P M ^b   Dorsten, Ferdi A ^b  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNILEVER RESEARCH VLAARDINGEN   (Netherlands)

Author keywords

Classification; Cross model validation; Permutation testing; PLSDA

Indexed keywords

EID: 38949180527     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-007-0099-6     Document Type: Article

References (27)

1. Anderssen, E., Dyrstad, K., Westad, F., & Martens, H. (2006). Reducing over-optimism in variable selection by cross-model validation. Chemometrics and Intelligent Laboratory Systems, 84 (1-2), 69-74.
2. Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17 (3), 166-173.
3. Bino, R. J., Hall, R. D., Fiehn, O., et al. (2004). Potential of metabolomics as a functional genomics tool. Trends in Plant Science, 9 (9), 418-425.
4. Bollard, M. E., Stanley, E. G., Lindon, J. C., Nicholson, J. K., & Holmes, E. (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR in Biomedicine, 18 (3), 143-162.
5. Breiman, L. (1996). Bagging predictors. Machine Learning, 24 (2), 123-140.
6. Breiman, L. (1998). Arcing classifiers. Annals of Statistics, 26 (3), 801-824.
7. Brereton, R. G. (2006). Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data. Trac-Trends in Analytical Chemistry, 25 (11), 1103-1111.
8. Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2 (4), 171-196.
9. Bylesjo, M., Rantalainen, M., Cloarec, O., et al. (2006). OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20 (8-10), 341-351.
10. Clayton, T. A., Lindon, J. C., Cloarec, O., et al. (2006). Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 440 (7087), 1073-1077.
11. Cloarec, O., Dumas, M. E., Craig, A., et al. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic H-1 NMR data sets. Analytical Chemistry, 77 (5), 1282-1289.
12. Fiehn, O. (2002). Metabolomics - the link between genotypes and phenotypes. Plant Molecular Biology, 48 (1-2), 155-171.
13. Golland, P., Liang, F., Mukherjee, S., & Panchenko, D. (2005). Permutation tests for classification. Lecture notes in Computer Science, 3559, 501-515.
14. Harrington, P. D. B. (2006). Statistical validation of classification and calibration models using bootstrapped Latin partitions. Trac-Trends in Analytical Chemistry, 25 (11), 1112-1124.
15. Martens, H., Dijksterhuis, G. B., & Byrne, D. V. (2000). Power of experimental designs, estimated by Monte Carlo simulation. Journal of Chemometrics, 14 (5-6), 441-462.
16. Mielke, P. W. Jr, & Berry, H. (2001). Permutation methods: A distance function approach. New York: Springer.
17. Ragazzi, E., Pucciarelli, S., Seraglia, R., et al. (2006). Multivariate analysis approach to the plasma protein profile of patients with advanced colorectal cancer. Journal of Mass Spectrometry, 41 (12), 1546-1553.
18. Rubingh, C. M., Bijlsma, S., Derks, E. P. P. A., et al. (2006). Assessing the performance of statistical validation tools for megavariate metabolomics data. Metabolomics, 2 (2), 53-61.
19. Stone, M. (1974). Cross validatory choice and assessement of statistical predictions. Journal of Royal Statistical Society B, 36, 111-147.
20. Trygg, J. (2002). O2-PLS for qualitative and quantitative analysis in multivariate calibration. Journal of Chemometrics, 16 (6), 283-293.
21. Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabonomics. Journal of Proteome Research, 6 (2), 469-479.
22. Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16 (3), 119-128.
23. Vaidyanathan, S., & Goodacre, R. (2007). Quantitative detection of metabolites using matrix-assisted laser desorption/ionization mass spectrometry with 9-aminoacridine as the matrix. Rapid Communications in Mass Spectrometry, 21 (13), 2072-2078.
24. Van Der Greef, J., & Smilde, A. K. (2005). Symbiosis of chemometrics and metabolomics: Past, present, and future. Journal of Chemometrics, 19 (5-7), 376-386.
25. van der Werf, M. J., Jellema, R. H., & Hankemeier, T. (2005). Microbial metabolomics: Replacing trial-and-error by the unbiased selection and ranking of targets. Journal of Industrial Microbiology & Biotechnology, 32 (6), 234-252.
26. Van Dorsten, F. A., Daykin, C. A., Mulder, T. P. J., & Van Duynhoven, J. P. M. (2006). Metabonomics approach to determine metabolic differences between green tea and black tea consumption. Journal of Agricultural and Food Chemistry, 54 (18), 6929-6938.
27. van Ommen, B. (2004). Nutrigenomics: Exploiting systems biology in the nutrition and health arenas. Nutrition, 20 (1), 4-8.