Chemometric variance analysis of 1H NMR metabolomics data on the effects of oral rinse on saliva

Metabolomics

Volumn 8, Issue , 2012, Pages 64-80

  Lemanska, Agnieszka ^a   Grootveld, Martin ^b   Silwood, Christopher J L ^c   Brereton, Richard G ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF BOLTON   (United Kingdom)

^c LONDON SOUTH BANK UNIVERSITY   (United Kingdom)

Author keywords

ASCA; Metabolomics; Multilevel analysis; NMR; Saliva

Indexed keywords

FLUORIDE SODIUM; MOUTHWASH;

ANALYSIS OF VARIANCE; ANALYTIC METHOD; ARTICLE; CHEMOMETRIC ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; HUMAN; HUMAN EXPERIMENT; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; METABOLOMICS; MONTE CARLO METHOD; NORMAL HUMAN; PRINCIPAL COMPONENT ANALYSIS; PROCESS DESIGN; PROCESS DEVELOPMENT; PROCESS TECHNOLOGY; PROTON NUCLEAR MAGNETIC RESONANCE; SALIVA ANALYSIS; VALIDATION STUDY;

BACTERIA (MICROORGANISMS);

EID: 84860366636     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-011-0358-4     Document Type: Article

References (51)

1. Ankenman, B. E., Aviles, A. I., & Pinheiro, J. C. (2003). Optimal designs for mixed-effects models with two random nested factors. Statistica Sinica, 13, 385-401.
2. Backhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A., & Gordon, J. I. (2005). Host-bacterial mutualism in the human intestine. Science, 307, 1915-1920.
3. Brereton, R. G. (2003). Chemometrics: Data analysis for the laboratory and chemical plant. Chichester, UK: Wiley.
4. Brereton, R. G. (2006). Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data. Trends in Analytical Chemistry, 25, 1103-1111.
5. Brereton, R. G. (2007). Applied Chemometrics for Scientists. Chichester: Wiley.
6. Brereton, R. G. (2009). Chemometrics for pattern recognition. Chichester: Wiley.
7. Brereton, R. G., & Lloyd, G. R. (2010). Support vector machines for classification and regression. Analyst, 135, 230-267.
8. Bro, R., & Smilde, A. K. (2003). Centering and scaling in component analysis. Journal of Chemometrics, 17, 16-33.
9. de Noord, O. E., & Theobald, E. H. (2005). Multilevel component analysis and multilevel PLS of chemical process data. Journal of Chemometrics, 19, 301-307.
10. Dixon, S. J., & Brereton, R. G. (2009). Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometrics and Intelligent Laboratory Systems, 95, 1-17.
11. Dixon, S. J., Xu, Y., Brereton, R. G., Soini, H. A., Novotny, M. V., Oberzaucher, E., et al. (2007). Pattern recognition of gas chromatography mass spectrometry of human volatiles in sweat to distinguish the sex of subjects and determine potential discriminatory marker peaks. Chemometrics and Intelligent Laboratory Systems, 87, 161-172.
12. Ferreira, D. L. S., Kittiwachana, S., Fido, L. A., Thompson, D. R., Escott, R. E. A., & Brereton, R. G. (2009). Multilevel simultaneous component analysis for fault detection in multicampaign process monitoring: Application to on-line high performance liquid chromatography of a continuous process. Analyst, 134, 1571-1585.
13. 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.
14. Hooper, L. V., Midtvedt, T., & Gordon, J. I. (2002). How host-microbial interactions shape the nutrient environment of the mammalian intestine. Annual Review of Nutrition, 22, 283-307.
15. Hox, J. J. (1995). Applied multilevel analysis. Amsterdam: TT-Publikaties.
16. 1H NMR metabolite profiling of feces as a tool to assess the impact of nutrition on the human microbiome. NMR in Biomedicine, 21, 615-626.
17. Jansen, J. J., Hoefsloot, H. C. J., Greef, J., Timmerman, M. E., & Smilde, A. K. (2005a). Multilevel component analysis of time-resolved metabolomics data. Analytica Chimica Acta, 530, 173-183.
18. Jansen, J. J., Hoefsloot, H. C. J., Greef, J., Timmerman, M. E., Westerhuis, J. A., & Smilde, A. K. (2005b). ASCA: Analysis of multivariate data obtained from an experimental design. Journal of Chemometrics, 19, 469-481.
19. Jansen, J. J., Bro, R., Hoefsloot, H. C. J., van Den Berg, F. W. J., Westerhuis, J. A., & Smilde, A. K. (2008). PARAFASCA: ASCA combined with PARAFAC for the analysis of metabolic fingerprinting data. Journal of Chemometrics, 22, 8.
20. Kiers, H. A. L., & ten Berge, J. M. F. (1989). Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations. Psychometrika, 54, 467-473.
21. Kiers, H. A. L., & ten Berge, J. M. F. (1994). Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure. British Journal of Mathematical and Statistical Psychology, 47, 109-126.
22. Kittiwachana, S., Ferreira, D. L. S., Fido, L. A., Thompson, D. R., Escott, R. E. A., & Brereton, R. G. (2010). Self-organizing map quality control index. Analytical Chemistry, 82, 5972-5982.
23. Kurokawa, K., Itoh, T., Kuwahara, T., Oshima, K., Toh, H., Toyoda, A., et al. (2007). Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Research, 14, 169-181.
24. Lefebvre, B. (2004). Intelligent bucketing for metabonomics, ACD/Labs technical note. Accessed November 23, 2010 from http://www. acdlabs. com/download/technotes/80/nmr/intelli_bucket. pdf.
25. Lloyd, G. R., Wongravee, K., Silwood, C. J. L., Grootveld, M. C., & Brereton, R. G. (2009). Self organising maps for variable selection: Application to human saliva analysed by nuclear magnetic resonance spectroscopy to investigate the effect of an oral healthcare product. Chemometrics and Intelligent Laboratory Systems, 98, 149-161.
26. Luciano, G., & Næs, T. (2009). Interpreting sensory data by combining principal component analysis and analysis of variance. Food Quality and Preference, 20, 167-175.
27. Massart, D. L., Vandeginste, B. G. M., Buydens, L. M. C., De Jong, S., Lewi, P. J., & Smeyers-Verbeke, J. (1997). Handbook of chemometrics and qualimetrics: Part A. Amsterdam: Elsevier.
28. Nueda, M. J., Conesa, A., Westerhuis, J. A., Hoefsloot, H. C. J., Smilde, A. K., Talón, M., et al. (2007). Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA. Bioinformatics, 23, 1792-1800.
29. Radford, J. R., Beighton, D., Nugent, Z., & Jackson, R. J. (1997). Effect of use of 0. 05% cetylpyridinium chloride mouthwash on normal oral flora. Journal of Dentistry, 25, 35-40.
30. Rosen, S. (1974). The microorganisms of the human mouth. The Journal of Prosthetic Dentistry, 31, 468-468.
31. Schaefer, M. L., Wongravee, K., Holmboe, M. E., Heinrich, N. M., Dixon, S. J., Zeskind, J. E., et al. (2010). Mouse urinary biomarkers provide signatures of maturation, diet, stress level, and diurnal rhythm. Chemical Senses, 35, 459-471.
32. Siemer, M., & Joormann, J. (2003). Power and measures of effect size in analysis of variance with fixed versus random nested factors. Psychological Methods, 8, 497-517.
33. Silwood, C. J. L., Lynch, E., Claxson, A. W. D., & Grootveld, M. C. (2002). H-1 and C-13 NMR spectroscopic analysis of human saliva. Journal of Dental Research, 81, 422-427.
34. Smilde, A. K., Jansen, J. J., Hoefsloot, H. C. J., Lamers, R., Greef, J., & Timmerman, M. E. (2005). ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21, 3043-3048.
35. Smilde, A. K., Hoefsloot, H. C. J., & Westerhuis, J. A. (2008). The geometry of ASCA. Journal of Chemometrics, 22, 464-471.
36. Smit, S., van Breemen, M. J., Hoefsloot, H. C. J., Smilde, A. K., Aerts, J. M. F. G., & de Koster, C. G. (2007). Assessing the statistical validity of proteomics based biomarkers. Analytica Chimica Acta, 592, 210-217.
37. Stahle, L., & Wold, S. (1989). Analysis of variance (ANOVA). Chemometrics and Intelligent Laboratory Systems, 6, 259-272.
38. Timmerman, M. E. (2006). Multilevel component analysis. British Journal of Mathematical and Statistical Psychology, 59, 301-320.
39. Timmerman, M. E., & Kiers, H. A. L. (2003). Four simultaneous component models of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika, 86, 105-122.
40. van Deun, K., Smilde, A. K., van der Werf, M., Kiers, H. A. L., & van Mechelen, I. (2009). A structured overview of simultaneous component based data integration. BMC Bioinformatics, 10, 246.
41. van Velzen, E. J. J., Westerhuis, J. A., van Duynhoven, J. P. M., van Dorsten, F. A., Hoefsloot, H. C. J., Jacobs, D. M., et al. (2008). Multilevel data analysis of a crossover designed human nutritional intervention study. Journal of Proteome Research, 7, 4483-4491.
42. Verouden, M. P. H., Westerhuis, J. A., van der Werf, M. J., & Smilde, A. K. (2009). Exploring the analysis of structured metabolomics data. Chemometrics and Intelligent Laboratory Systems, 98, 88-96.
43. Vis, D., Westerhuis, J. A., Smilde, A. K., & van der Greef, J. (2007). Statistical validation of megavariate effects in ASCA. BMC Bioinformatics, 8, 322-329.
44. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D., Smilde, A. K., van Velzen, E., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4, 81-89.
45. Westerhuis, J. A., van Velzen, E., Hoefsloot, H. C. J., & Smilde, A. K. (2010). Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics, 6, 119-128.
46. Wilderjans, T. F., Ceulemans, E., van Mechelen, I., & van den Berg, R. A. (2011). Simultaneous analysis of coupled data matrices subject to different amounts of noise. British Journal of Mathematical and Statistical Psychology, 64, 277-290.
47. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2, 37-52.
48. Wongravee, K., Lloyd, G. R., Hall, J., Holmboe, M., Schaefer, M. L., Reed, R. R., et al. (2009a). Monte-Carlo methods for determining optimal number of significant variables. Application to mouse urinary profiles. Metabolomics, 5, 387-406.
49. Wongravee, K., Lloyd, G. R., Silwood, C. J., Grootveld, M. C., & Brereton, R. G. (2009b). Supervised self organizing maps for classification and determination of potentially discriminatory variables: Illustrated by application to nuclear magnetic resonance metabolomic profiling. Analytical Chemistry, 82, 628-638.
50. Xu, Y., Brereton, R. G., Trebesius, K., Bergmaier, I., Oberzaucher, E., Grammer, K., et al. (2007a). A fuzzy distance metric for measuring the dissimilarity of planar chromatographic profiles with application to denaturing gradient gel electrophoresis data from human skin microbes: Demonstration of an individual and gender-based fingerprint. Analyst, 132, 638-646.
51. Xu, Y., Dixon, S. J., Brereton, R. G., Soini, H. A., Novotny, M. V., Trebesius, K., et al. (2007b). Comparison of human axillary odour profiles obtained by gas chromatography/mass spectrometry and skin microbial profiles obtained by denaturing gradient gel electrophoresis using multivariate pattern recognition. Metabolomics, 3, 427-437.