Canonical correlation analysis for multivariate regression and its application to metabolic fingerprinting

Biochemical Engineering Journal

Volumn 40, Issue 2, 2008, Pages 199-204

  Yamamoto, Hiroyuki ^a   Yamaji, Hideki ^a   Fukusaki, Eiichiro ^b   Ohno, Hiromu ^a   Fukuda, Hideki ^a  

^a KOBE UNIVERSITY   (Japan)

^b OSAKA UNIVERSITY   (Japan)

Author keywords

Canonical correlation analysis; Kernel method; Metabolic fingerprinting; Metabolomics; Multivariate analysis; Partial least squares

Indexed keywords

BIOCHEMICAL ENGINEERING; CORRELATION METHODS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MULTIVARIABLE CONTROL SYSTEMS; REGRESSION ANALYSIS;

CANONICAL CORRELATION ANALYSIS; KERNEL METHOD; METABOLIC FINGERPRINTING; METABOLOMICS; MULTIVARIATE ANALYSIS; PARTIAL LEAST SQUARES;

METABOLISM;

ARTICLE; CORRELATION ANALYSIS; KERNEL METHOD; MATHEMATICAL MODEL; METABOLOMICS; MULTIVARIATE ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; TEA;

EID: 43849103994     PISSN: 1369703X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bej.2007.12.009     Document Type: Article

References (25)

1. Fukusaki E., and Kobayashi A. Plant metabolomics: potential for practical operation. J. Biosci. Bioeng. 100 (2005) 347-354
2. Fiehn O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genom. 2 (2001) 155-168
3. Raamsdonk L.M., Teusink B., Broadhurst D., Zhang N., Hayes A., Walsh M.C., Berden J.A., Brindle K.M., Kell D.B., Rowland J.J., Westerhoff H.V., van Dam K., and Oliver S.G. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19 (2001) 45-50
4. Antoniewicz M.R., Stephanopoulos G., and Kelleher J.K. Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway. Metabolomics 2 (2006) 41-52
5. Gullberga J., Jonssonb P., Nordströma A., Sjöströmb M., and Moritz T. Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal. Biochem. 331 (2004) 283-295
6. Wold S., Sjöström M., and Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58 (2001) 109-130
7. Boulesteix A., and Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform. 8 (2007) 32-44
8. Nguyen D., and Rocke D. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18 (2002) 39-50
9. Boulesteix A. PLS dimension reduction for classification with microarray data. Stat. Appl. Genet. Mol. Biol. 3 (2004) Article 33
10. Hardoon D.R., Szedmak S., and Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16 (2004) 2639-2664
11. Rosipal R., and Krämer N. Overview and recent advances in partial least squares. In: Saunders C., Grobelnik M., Gunn S., and Shawe-Taylor J. (Eds). Subspace, Latent Structure and Feature Selection Techniques (2006), Springer, New York 34-51
12. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, Cambridge
13. Schölkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press, Cambridge
14. Schölkopf B., Tsuda K., and Vert J.P. Kernel Methods in Computational Biology (2004), MIT Press, Cambridge
15. Schölkopf B., Smola A.J., and Müller K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 (1998) 1299-1319
16. Mika S., Rätsch G., Weston J., Schölkopf B., and Müller K.R. Fisher discriminant analysis with kernels. Neural Netw. Signal Process. IX (1999) 41-48
17. Rosipal R., and Trejo L.J. Kernel partial least squares regression in reproducing kernel Hilbert space. JMLR 2 (2001) 97-123
18. S. Akaho, A kernel method for canonical correlation analysis, International Meeting of Psychometric Society, 2001.
19. Bach F.R., and Jordan M.I. Kernel independent component analysis. JMLR 3 (2002) 1-48
20. Lindgren F., Geladi P., and Wold S. The kernel algorithm for PLS. J. Chemom. 7 (1993) 45-59
21. Pongsuwan W., Fukusaki E., Bamba T., Yonetani T., Yamahara T., and Kobayashi A. Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting. J. Agric. Food Chem. 55 (2007) 231-236
22. M. Borga, T. Landelius, H. Knutsson, A unified approach to PCA, PLS, MLR and CCA, Technical Report, Linköping University, 1997.
23. J.A. Wegelin, A survey of partial least squares (PLS) methods, with emphasis on the two-block case, Technical Report, University of Washington, 2000.
24. Cogdilla R.P., and Dardenneb P. Least-squares support vector machines for chemometrics: an introduction and evaluation. J. Near Infrared Spectrosc. 12 (2004) 93-100
25. Shinzawa H., Jiang J.H., Ritthiruangdej P., and Ozaki Y. Investigations of bagged kernel partial least squares (KPLS) and boosting KPLS with applications to near-infrared (NIR) spectra. J. Chemom. 20 (2006) 436-444