Non-negative principal component analysis for NMR-based metabolomic data analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 118, Issue , 2012, Pages 51-61

  Deng, Lingli ^a   Cheng, Kian Kai ^b,c   Dong, Jiyang ^a,b   Griffin, Julian L ^b   Chen, Zhong ^a  

^a XIAMEN UNIVERSITY   (China)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

K+1 rule; Local feature representation; Multivariate data analysis; NMR based metabolomics; Non negative principal component analysis

Indexed keywords

CITRIC ACID; CREATININE; FORMIC ACID; HIPPURIC ACID; SUCCINIC ACID;

ALGORITHM; ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; DATA EXTRACTION; DISCRIMINANT ANALYSIS; FOURIER TRANSFORMATION; HUMAN; HYPERTHYROIDISM; METABOLOMICS; MULTIVARIATE ANALYSIS; NOISE; NON NEGATIVE PRINCIPAL COMPONENT ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; SIMULATION;

EID: 84865414572     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2012.07.011     Document Type: Article

References (26)

1. Bollard M.E., Stanley E.G., Lindon J.C., Nicholson J.K., Holmes E. NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR in Biomedicine 2005, 18:143-162.
2. Griffin J.L. Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Current Opinion in Chemical Biology 2003, 7:648-654.
3. Coen M., Holmes E., Lindon J.C., Nicholson J.K. NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chemical Research in Toxicology 2008, 21:9-27.
4. 1H-NMR-based metabonomic analysis of metabolic profiling in diabetic nephropathy rats induced by streptozotocin. American Journal of Physiology. Renal Physiology 2011, 300:947-956.
5. 1 H-NMR-based metabonomics. Nature Medicine 2002, 8:1439-1445.
6. Lindon J.C., Holmes E., Bollard M.E., Stanley E.G., Nicholson J.K. Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 2004, 9:1-31.
7. Wold S. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 1987, 2:37-52.
8. Wang G.Q., Ding Q.Z., Hou Z.Y. Independent component analysis and its applications in signal processing for analytical chemistry. TRAC Trends in Analytical Chemistry 2008, 27:368-376.
9. Hyvärinen A., Oja E. Independent component analysis: algorithms and applications. Neural Networks 2000, 13:411-430.
10. Scholz M., Gatzek S., Sterling A., Fiehn O., Selbig J. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 2004, 20:2447-2454.
11. Lee D.D., Seung H.S. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 2001, 13:1561-1568.
12. Climaco-Pinto R., Barros A.S., Locquet N., Schmidtke L., Rutledge D.N. Improving the detection of significant factors using ANOVA-PCA by selective reduction of residual variability. Analytica Chimica Acta 2009, 653:131-142.
13. Hoyer P.O. Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research 2004, 5:1457-1469.
14. Bach F.R., Jordan M.I. Kernel independent component analysis. Journal of Machine Learning Research 2003, 3:1-48.
15. Lee D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401:788-791.
16. Han X. Non-negative principal component analysis for cancer molecular pattern discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2010, 7:537-549.
17. Allen G.I., Maletić-Savatić M. Sparse non-negative generalized PCA with applications to metabolomics. Bioinformatics 2011, 27:3029-3035.
18. Zass R., Shashua A. Nonnegative sparse PCA. Advances in Neural Information Processing Systems 2007, 19:1561-1568.
19. Maher A.D., Crockford D., Toft H., Malmodin D., Faber J.H., McCarthy M.I., Barrett A., Allen M., Walker M., Holmes E. Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes. Analytical Chemistry 2008, 80:7354-7362.
20. Muncey H.J., Jones R., De Iorio M., Ebbels T.M.D. MetAssimulo: simulation of realistic NMR metabolic profiles. BMC Bioinformatics 2010, 11:496.
21. Cloarec O., Dumas M.E., Craig A., Barton R.H., Trygg J., Hudson J., Blancher C., Gauguier D., Lindon J.C., Holmes E. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry 2005, 77:1282-1289.
22. Wei Y.Y., Wang C.X., Li W., Dong J.Y., Chen Z. NMR-based metabonomics study on serum and urine of hyperthyroidism. Chemical Journal of Chinese Universities 2010, 31:279-284.
23. 1H NMR metabonomics. Analytical Chemistry 2006, 78:4281-4290.
24. He X.F., Niyogi P. Locality Preserving Projections. Advances in Neural Information Processing Systems 2006, 16:153-160.
25. Balla D.Z., Faber C. In vivo intermolecular zero-quantum coherence MR spectroscopy in the rat spinal cord at 17.6 T: a feasibility study. Magnetic Resonance Materials in Physics, Biology and Medicine 2007, 20:183-191.
26. Greiner M., Pfeiffer D., Smith R. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive Veterinary Medicine 2000, 45:23-41.