Bayesian mass spectra peak alignment from mass charge ratios

Cancer Informatics

Volumn 6, Issue , 2008, Pages 217-241

  Liu, Junfeng ^a   Yu, Weichuan ^b   Wu, Baolin ^c   Zhao, Hongyu ^d  

^a WEST VIRGINIA UNIVERSITY   (United States)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^c UNIVERSITY OF MINNESOTA   (United States)

^d YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Mass spectrometry; Peak alignment; Random grafting pruning Markov chain Monte Carlo (RGPCMC); Reversible jump Markov chain Monte Carlo (RJMCMC); Sample classification; Symmetric transition

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; BAYES THEOREM; DATA COLLECTION METHOD; DISEASE SEVERITY; INFORMATION PROCESSING; MASS SPECTROMETRY; MATRIX ASSISTED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY; MEDICAL RESEARCH; METHODOLOGY; MONTE CARLO METHOD; OVARY CANCER; PROBABILITY; PROGNOSIS; PROTEIN ANALYSIS; TUNING CURVE; VALIDATION STUDY;

EID: 49649126084     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510800600006     Document Type: Article

References (36)

1. Barry, D. and Hartigan, J.A. 1992. Product partition models for change point problems. The Annals of Statistics, Vol. 20, No. 1:260-279.
2. Barry, D. and Hartigan, J.A. 1993. A Bayesian analysis for change point problems. Journal of the American Statistical Association, Vol. 88, No. 421:309-319.
3. Cappé, O., Robert, C.P. and Rydén, T. 2003. Reversible jump, birth-anddeath and more general continuous time Markov chain Monte Carlo samplers. Journal of the Royal Statistical Society Series B., Vol. 65, No. 3:679-700.
4. Coombes, K.R., Fritsche, H.A. Jr., Clarke, C., Chen, J., Baggerly, K.A., Morris, J.S., Xiao, L., Hung, M. and Kuerer, H.M. 2003. Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clinical Chemistry, Vol. 49, No. 10:1615-1623.
5. Coombes, K.R., Tsavachidis, S., Morris, J.S., Baggerly, K.A., Hung, M.-C. and Kuerer, H.M. 2005a. 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, Vol. 5, No. 16:4107-4117.
6. Coombes, K.R., Koomen, J.M., Baggerly, K.A., Morris, J.S. and Kobayashi, R. 2005b. Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Informatics, Vol. 1, No. 1:41-52.
7. Denison, D.G.T., Holmes, C.C., Mallick, B.K. and Smith, A.F.M. 2002. Bayesian Methods for Nonlinear Classification and Regression, John Wiley & Sons, Ltd.
8. Eilers, P.H.C. 2004. Parametric time warping. Analytical Chemistry, Vol. 76, No. 2:404-411.
9. Fearnhead, P. 2005. Exact Bayesian curve fitting and signal segmentation. IEEE Transactions on Signal Processing, Vol. 53, No. 6:2160-2166.
10. Gelfand, A.E. and Smith, A.F.M. 1990. Sampling-based approaches to calculating marginal densities. Journal of American Statistical Association, Vol. 85, No. 410:398-409.
11. Green, P.J. 1995. Reversible jump markov chain monte carlo computation and Bayesian model determination. Biometrika, Vol. 82, No. 4:711-732.
12. Green, P.J. and Mardia, K.V. 2006. Bayesian alignment using hierarchical models with applications in protein bioinformatics. Biometrika, Vol. 93, No. 2:235-254.
13. Guan, Z. 2004. A semiparametric changepoint model. Biometrika, Vol. 91, No. 4:849-862.
14. Johnson, K.J., Wright, B.W., Jarman, K.H. and Synovec, R.E. 2003. High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis. Journal of Chromatography A, 996:141-155.
15. Knorr-Held, L. and Raßer, G. 2000. Bayesian detection of clusters and discontinuities in disease maps. Biometrics, Vol. 56, No. 1:13-21.
16. Link, A.J., Eng, J., Schieltz, D.M., Carmack, E., Mize, G.J., Morris, D.R., Garvik, B.M. and Yates, J.R. III. 1999. Direct analysis of protein complexes using mass spectrometry. Nature Biotechnology, 17:676-682.
17. Liu, J., Harner, E.J. and Yang, H. 2006. Bayesian circle segmentation with application to DNA copy number alteration detection, submitted manuscript.
18. Loschi, R.H., Cruz, F.R.B. and Arellano-Valle, R.B. 2005. Multiple change point analysis for the regular exponential family using the product partition model. Journal of Data Science, Vol. 3, No. 3:305-330.
19. Morris, J.S., Coombes, K.R., Kooman, J., Baggerly, K.A. and Kobayashi, R. 2005. Feature extraction and quantification for mass spectrometry data in biomedical applications using the mean spectrum. Bioinformatics, Vol. 21, No. 9:1764-1775.
20. Nielsen, N.V., Carstensen, J.M. and Smedsgaard, J. 1998. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, 805:17-35.
21. Olshen, A.B. and Venkatraman, E.S. 2004. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, Vol. 5, No. 4:557-572.
22. Pollard, K. and van der Laan, M. 2002. A method to identify significant clusters in gene expression data. SCI2002 Proceedings, Volumn II: 318-325, Orlando 2002, International Institute of Informatics and Systems.
23. Randolph, T.W. and Yasui, Y. 2006. Multiscale processing of mass spectrometry data. Biometrics, Vol. 62, No. 2:589-97.
24. Richardson, S. and Green, P.J. 1997. On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society B, Vol. 59, No. 4:731-792.
25. Satten, G.A., Datta, S., Moura, H., Woolfitt, A.R., Carvalho, G., Facklam, R. and Barr, J.R. 2004. Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. Bioinformatics, Vol. 20, No. 17:3128-3136.
26. Stephens, M. 2000a. Bayesian analysis of mixture models with an unknown number of components - An alternative to reversible jump methods, The Annals of Statistics, Vol. 28, No. 1:40-74.
27. Stephens, M. 2000b. Dealing with label switching in mixture models. Journal of the Royal Statistical Society B, Vol. 62, No. 4:795-809.
28. Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S., Shi, G., Koong, A. and Le, Q.T. 2004. Sample classification from protein mass spectrometry by 'peak probability contrasts'. Bioinformatics, Vol. 20, No. 17:3034-3044.
29. Torgrip, R.J.O., Aberg, M., Karlberg, B. and Jacobsson, S.P. 2003. Peak alignment using reduced set mapping. Journal of Chemometrics, 17:573-582.
30. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. 2003. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics, Vol. 19, No. 13:1636-1643.
31. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. 2006. Ovarian cancer classification based on mass spectrometry analysis of sera. Cancer Informatics 2:123-132.
32. Yao, Y. 1984. Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches. Annals of Statistics, Vol. 12, No. 4: 1434-1447.
33. Yasui, Y., Pepe, M., Thompson, M.L., Adam, B.-L., Wright, G.L., Qu, Y., Potter, J.D., Winget, M., Thornquist, M. and Feng, Z. 2003. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics, Vol. 4, No. 3:449-463.
34. Yu, W., Li, X., Liu, J., Wu, B., Williams, K. and Zhao, H. 2006a. Multiple peak alignment in sequential data analysis: A scale-space based approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 3, No. 3:208-219.
35. Yu, W., Wu, B., Lin, N., Stone, K., Williams, K. and Zhao, H. 2006b. Detecting and aligning peaks in mass spectrometry data with applications to MALDI. Computational Biology and Chemistry, 30:27-38.
36. Yu, W., Wu, B., Liu, J., Li, X., Stone, K., Williams, K. and Zhao, H. 2005 MALDI-MS data analysis for disease biomarker discovery. New and Emerging Proteomics Techniques, Humana Press, 328:199-216.