Multiblock principal component analysis: An efficient tool for analyzing metabolomics data which contain two influential factors

Metabolomics

Volumn 8, Issue , 2012, Pages 37-51

  Xu, Yun ^a   Goodacre, Royston ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

ANOVA PCA; Consensus PCA; Experiment design; Metabolomics; Multiblock PCA; Simulation

Indexed keywords

ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DISCRIMINANT ANALYSIS; MASS SPECTROMETRY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; METABOLOMICS; MULTIBLOCK PRINCIPAL COMPONENT ANALYSIS; PERFORMANCE MEASUREMENT SYSTEM; PRINCIPAL COMPONENT ANALYSIS; PROCESS DESIGN; PROCESS MODEL; SCORING SYSTEM; VALIDATION PROCESS;

EID: 84860373798     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-011-0361-9     Document Type: Article

References (17)

1. Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 203-219.
2. Brereton, R. G. (2003). Chemometrics: Data analysis for the laboratory and chemical plant. Chichester: Wiley.
3. Climaco-Pinto, R., Barros, A. S., Locquet, N., Schmidtke, L., & Rutledge, D. N. (2009). Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability. Analytica Chimica Acta, 653, 131-142.
4. Ferreira, D. L. S., Kittiwachana, S., Fido, L. A., Thompson, D. R., Escott, R. E. A., & Brereton, R. G. (2010). Windows consensus PCA for multiblock statistical process control: Adaption to small and time dependent normal operating condition regions, illustrated by on-line high performance liquid chromatography of a three stage continuous process. Journal of Chemometrics, 24, 596-609.
5. Good, P. I. (2005). Permutation, parametric and bootstrap tests of hypotheses (3rd ed.). New York: Springer.
6. Gower, J. C., & Dijksterhuis, G. B. (2004). Procrustes problems. Oxford: Oxford University Press.
7. Harrington, P. B., Vieira, N. E., Espinoza, J., Nien, J. K., Romero, R., & Yergey, A. L. (2005). Analysis of variance-principal component analysis: A soft tool for proteomic discovery. Analytica Chimica Acta, 544, 118-127.
8. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.
9. Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.
10. Kassama, Y., Xu, Y., Dunn, W. B., Geukens, N., Anné, J., & Goodacre, R. (2010). Assessment of adaptive focused acoustics versus manual vortex/freeze-thaw for intracellular metabolite extraction from Streptomyces lividans producing recombinant proteins using GC-MS and multiblock principal component analysis. Analyst, 135, 934-942.
11. Manly, B. F. (2005). Multivariate statistical methods: A primer. London: Chapman & Hall.
12. Qin, S. J., Valle, S., & Piovoso, M. J. (2001). On unifying multiblock analysis with application to decentralized process monitoring. Journal of Chemometrics, 15, 715-742.
13. 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, 21, 3043-3048.
14. Smilde, A. K., Westerhuis, J. A., & de Jong, S. (2003). A framework for sequential multiblock component methods. Journal of Chemometrics, 17, 323-337.
15. Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1998). Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 12, 301-321.
16. Xu, Y., Cheung, W., Winder, C. L., & Goodacre, R. (2010). VOC-based metabolic profiling for food spoilage detection with the application to detecting Salmonella typhimurium contaminated pork. Analytical and Bioanalytical Chemistry, 397, 2439-2449.
17. Zomer, S., Dixon, S. J., Xu, Y., Jensen, S. P., Wang, H., Lanyon, C. V., et al. (2009). Consensus multivariate methods in gas chromatographic mass spectrometry and denaturing gradient gel electrophoresis: MHC-congenic and other strains of mice can be classified according to the profiles of volatiles and microflora in their scent-marks. Analyst, 134, 114-123.