Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data

Proteomics

Volumn 10, Issue 14, 2010, Pages 2564-2572

  Mostacci, Elise ^a,b,c   Truntzer, Caroline ^a,d   Cardot, Hervé ^b,c   Ducoroy, Patrick ^a,d  

^a CLIPP (Clinical Innovation Proteomic Platform)   (France)

^b UNIVERSITÉ DE BOURGOGNE   (France)

^c UNIVERSITÉ DE BOURGOGNE   (France)

^d DIJON UNIVERSITY HOSPITAL   (France)

Author keywords

Bioinformatics; Clinical proteomics; Denoising; Mass spectrometry; Principal component analysis; Wavelets

Indexed keywords

ANALYTIC METHOD; ARTICLE; CONTROLLED STUDY; MASS SPECTROMETRY; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEOMICS; SIMULATION;

DATABASES, PROTEIN; HUMANS; MASS SPECTROMETRY; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 77954710902     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.200900185     Document Type: Article

References (27)

1. Coombes, K. R., Baggerly, K. A., Morris, J. S., in: Dubitzky, M., Granzow, M., Berrar, D. (Eds.), Pre-Processing Mass Spectrometry Data, Fundamentals of Data Mining in Genomics and Proteomics, Kluwer, Boston 2005.
2. Antoniadis, A., Bigot, J., Sapatinas, T., Wavelet estimators in nonparametric regression: a comparative simulation study. J. Stat. Softw. Am. Stat. Assoc. 2001, 6, 1-83.
3. Coombes, K. R., Tsavachidis, S., Morris, J. S., Baggerly, K. A. et al., Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 2005, 5, 4107-4117. (Pubitemid 41626078)
4. Du, P., Kibbe, W. A., Lin, S. M., Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22, 2059-2065. (Pubitemid 44390894)
5. Morris, J. S., Coombes, K. R., Koomen, J., Baggerly, K. A., Kobayashi, R., Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 2005, 21, 1764-1775. (Pubitemid 40674202)
6. Jolliffe, I. T., Principal Component Analysis, 2nd Edn, Springer, New York 2002.
7. Aminghafari, M., Cheze, N., Poggi, J.-M., Multivariate denoising using wavelets and principal component analysis. Comput. Stat. Data Anal. 2006, 50, 2381-2398.
8. Jolliffe, I., Trendafilov, N., Uddin, M., A modified principal component technique based on the lasso. J. Comput. Graph. Stat. 2003, 12, 531-547.
9. Cadima, I., Jolliffe, I., Loadings and correlations in interpretation of principal components. J. Appl. Stat. 2005, 22, 203-214.
10. Shen, H., Huang, J. Z., Sparse principal component analysis via regularized low rank matrix approximation. J. Multivariate Anal. 2008, 99, 1015-1034.
11. Zou, H., Hastie, T., Tibshirani, R., Sparse principal component analysis. J. Comput. Graph. Stat. 2006, 15, 262-286.
12. Daubechies, I., Ten Lectures On Wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics 1992.
13. Mallat, S., A Wavelet Tour of Signal Processing. Academic Press, San Diego 1999.
14. Donoho, D. L., Johnstone, I. M., Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994, 81, 425-455.
15. Coifman, R. R., Donoho, D. L., Translation-invariant de-noising. Lect. Notes Stat. 1995, 103, 125-150.
16. Stark, J. L., Fadili, J., Murtagh, F., The undecimated wavelet decomposition and its reconstruction. IEEE Trans. Image Process. 2007, 16, 297-309.
17. Kwon, D., Vannucci, M., Song, J. J., Jeong, J., Pfeiffer, R. M., A novel wavelet-based thresholding method for the pre-processing of mass spectrometry data that accounts for heterogeneous noise. Proteomics 2008, 8, 3019-3029.
18. Tibshirani, R., Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 1996, 58, 267-288.
19. Zou, H., Hastie, T., Regression shrinkage and selection via the elastic net, with applications to microarrays, Technical report, Department of Statistics, Standford University 2003.
20. Zou, H., Hastie, T., Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 2005, 67, 301-320.
21. Zou, H., Hastie, T., Elasticnet: elastic net regularization and variable selection, R package version 1.0.4, 2007.
22. Du, P., Kibbe, W., Lin, S., MassSpecWavelet: mass spectrum processing by wavelet-based algorithms, R package version 1.10.0, 2009.
23. Cruz-Marcelo, A., Guerra, R., Vannucci, M., Li, Y. et al., Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. BMC Bioinformatics 2008, 24, 2129-2136.
24. Meuleman, W., Engwegen, J. Y. M. N., Gast, M.-C. W., Beijnen, J. H. et al., Comparison of normalization methods for SELDI-TOF mass spectrometry data. BMC Bioinformatics 2008, 9.
25. Cattell, R. B., The scree test for the number of factors. Multivariate Behav. Res. 1996, 1, 245-276.
26. Lê Cao, K., Rossouw, D., Robert-Granié, C., Besse, P., A sparse PLS for variable selection when integrating Omics data. Stat. Appl. Genet. Mol. Biol. 2008, 7.
27. Lê Cao, K., Martin, P. G. P., Robert-Granié, C., Besse, P., Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 2009, 10.