A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels

BMC Bioinformatics

Volumn 13, Issue 1, 2012, Pages

  Wu, Steven H ^a,b,e   Black, Michael A ^c   North, Robyn A ^d   Rodrigo, Allen G ^a,b,f  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

^c UNIVERSITY OF OTAGO   (New Zealand)

^d KING'S COLLEGE LONDON   (United Kingdom)

^e DUKE UNIVERSITY   (United States)

^f NATIONAL EVOLUTIONARY SYNTHESIS CENTER   (United States)

Author keywords

Differentially expressed protein; Global Bayesian model; Markov chain Monte Carlo (MCMC); Two dimensional polyacrylamide gel electrophoresis (2D PAGE)

Indexed keywords

2D-PAGE; BAYESIAN MODEL; CONTROL GROUPS; DIFFERENTIAL EXPRESSIONS; EXPRESSION INTENSITIES; FALSE DISCOVERY RATE; GLOBAL DISTRIBUTION; GLOBAL MODELS; INDIVIDUAL PROTEINS; LIKELIHOOD FUNCTIONS; LIKELIHOOD RATIO TESTS; MARGINAL POSTERIOR DISTRIBUTION; MARKOV CHAIN MONTE CARLO; MISSING VALUES; PROBABILISTIC MODELS; PROTEIN EXPRESSIONS; SIMULATION ANALYSIS; SOURCE CODES; STATISTICAL APPROACH; TWO-DIMENSIONAL POLYACRYLAMIDE GEL ELECTROPHORESIS; UNIVARIATE; WHETHER DIFFERENCES;

BAYESIAN NETWORKS; ELECTROPHORESIS; GEOGRAPHICAL DISTRIBUTION; PROBABILITY DISTRIBUTIONS; STATISTICAL TESTS; TWO DIMENSIONAL;

PROTEINS;

ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; MONTE CARLO METHOD; PROBABILITY; PROTEIN SYNTHESIS; PROTEOMICS; STATISTICAL MODEL; STATISTICS; TWO DIMENSIONAL GEL ELECTROPHORESIS;

BAYES THEOREM; COMPUTER SIMULATION; ELECTROPHORESIS, GEL, TWO-DIMENSIONAL; LIKELIHOOD FUNCTIONS; MARKOV CHAINS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MONTE CARLO METHOD; PROBABILITY; PROTEIN BIOSYNTHESIS; PROTEOMICS;

EID: 84862274591     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-13-137     Document Type: Article

References (24)

1. O'Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975, 250(10):4007-4021. 2874754, 236308.
2. Morris JS, Baladandayuthapani V, Herrick RC, Sanna P, Gutstein H. Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data. The Annals of Applied Statistics 2011, 5:894-923. 10.1214/10-AOAS407, 3298181, 22408711.
3. Dowsey AW, Dunn MJ, Yang G-Z. The role of bioinformatics in two-dimensional gel electrophoresis. Proteomics 2003, 3(8):1567-1596. 10.1002/pmic.200300459, 12923783.
4. Berth M, Moser FM, Kolbe M, Bernhardt J. The state of the art in the analysis of two-dimensional gel electrophoresis images. Appl Microbiol Biotechnol 2007, 76(6):1223-1243. 10.1007/s00253-007-1128-0, 2279157, 17713763.
5. Chang J, Van Remmen H, Ward WF, Regnier FE, Richardson A, Cornell J. Processing of data generated by 2-dimensional gel electrophoresis for statistical analysis: missing data, normalization, and statistics. J Proteome Res 2004, 3(6):1210-1218. 10.1021/pr049886m, 15595730.
6. Biron DG, Brun C, Lefevre T, Lebarbenchon C, Loxdale HD, Chevenet F, Brizard JP, Thomas F. The pitfalls of proteomics experiments without the correct use of bioinformatics tools. Proteomics 2006, 6(20):5577-5596. 10.1002/pmic.200600223, 16991202.
7. Jacobsen S, Grove H, Nedenskov Jensen K, Sørensen HA, Jessen F, Hollung K, Uhlen AK, Jørgensen BM, Færgestad EM, Søndergaard I. Multivariate analysis of 2-DE protein patterns - Practical approaches. Electrophoresis 2007, 28(8):1289-1299. 10.1002/elps.200600414, 17351893.
8. Grove H, Hollung K, Uhlen AK, Martens H, Faergestad EM. Challenges related to analysis of protein spot volumes from two-dimensional gel electrophoresis as revealed by replicate gels. J Proteome Res 2006, 5(12):3399-3410. 10.1021/pr0603250, 17137341.
9. Wu SH, Black MA, North RA, Atkinson KR, Rodrigo AG. A statistical model to identify differentially expressed proteins in 2D PAGE gels. PLoS Comput Biol 2009, 5(9):e1000509. 10.1371/journal.pcbi.1000509, 2734266, 19763172.
10. Wheelock T, Buckpitt AR. Software-induced variance in two-dimensional gel electrophoresis image analysis. Electrophoresis 2005, 26(23):4508-4520. 10.1002/elps.200500253, 16315176.
11. Albrecht D, Kniemeyer O, Brakhage AA, Guthke R. Missing values in gel-based proteomics. Proteomics 2010, 10(6):1202-1211. 10.1002/pmic.200800576, 20077407.
12. Krogh M, Fernandez C, Teilum M, Bengtsson S, James P. A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments. J Proteome Res 2007, 6(8):3335-3343. 10.1021/pr070137p, 17625817.
13. Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 1970, 57(1):97-109.
14. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of State Calculations by Fast Computing Machines. J Chem Phys 1953, 21(6):1087-1092.
15. Atkinson K. Proteomic biomarker discovery for preeclampsia.PhD thesis 2008, Auckland: University of Auckland.
16. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006, 1:515-533.
17. Roberts GO, Gelman A, Gilks WR. Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms. Ann Appl Probab 1997, 7(1):110-120.
18. Roberts GO, Rosenthal JS. Optimal Scaling for Various Metropolis-Hastings Algorithms. Stat Sci 2001, 16(4):351-367.
19. Roberts GO, Sahu SK. Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler. Journal of the Royal Statistical Society Series B (Methodological) 1997, 59(2):291-317.
20. Liu C, Rubin DB, Wu YN. Parameter expansion to accelerate EM: The PX-EM algorithm. Biometrika 1998, 85(4):755-770.
21. Rambaut A, Drummond A. Tracer v1.4.1 2007, Available from http://beast.bio.ed.ac.uk/Tracer.
22. Binder H, Schumacher M. Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinforma 2009, 10(1):18.
23. Huelsenbeck JP, Ronquist F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 2001, 17(8):754-755. 10.1093/bioinformatics/17.8.754, 11524383.
24. Altekar G, Dwarkadas S, Huelsenbeck JP, Ronquist F. Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics 2004, 20(3):407-415. 10.1093/bioinformatics/btg427, 14960467.