A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Journal of Theoretical Biology

Volumn 264, Issue 2, 2010, Pages 211-222

  Ogunnaike, Babatunde A ^a   Gelmi, Claudio A ^b   Edwards, Jeremy S ^c,d  

^a University of Delaware   (United States)

^b PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

^c UNIVERSITY OF NEW MEXICO HEALTH SCIENCES CENTER   (United States)

^d University of New Mexico   (United States)

Author keywords

Beta distributions; Gamma distributions; Gene expression; Mixture models; Poisson distributions

Indexed keywords

BACTERIUM; DATA SET; DNA; GENE EXPRESSION; NUMERICAL MODEL; POISSON RATIO; PROBABILITY; STATISTICAL ANALYSIS; THEORETICAL STUDY;

ARTICLE; CONCEPTUAL FRAMEWORK; DEINOCOCCUS RADIODURANS; DNA DAMAGE; DNA MICROARRAY; GENE EXPRESSION REGULATION; INFERENTIAL STATISTICS; MATHEMATICAL COMPUTING; MICROARRAY ANALYSIS; MICROBIAL POPULATION DYNAMICS; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; SYSTEM ANALYSIS;

ALGORITHMS; DEINOCOCCUS; DNA DAMAGE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, BACTERIAL; GENOME, BACTERIAL; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROBABILITY;

DEINOCOCCUS RADIODURANS;

EID: 77951652898     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2010.02.021     Document Type: Article

References (36)

1. Allison D.B., Cui X.Q., Page G.P., Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nature Reviews Genetics 2006, 7:55-65.
2. Allison D.B., Gadbury G.L., Heo M.S., Fernandez J.R., Lee C.K., Prolla T.A., Weindruch R. A mixture model approach for the analysis of microarray gene expression data. Computational Statistics & Data Analysis 2002, 39:1-20.
3. Baggerly K.A., Coombes K.R., Hess K.R., Stivers D.N., Abruzzo L.V., Zhang W. Identifying differentially expressed genes in cDNA microarray experiments. Journal of Computational Biology 2001, 8:639-659.
4. Brenner S., Johnson M., Bridgham J., Golda G., Lloyd D.H., Johnson D., Luo S., McCurdy S., Foy M., Ewan M., Roth R., George D., Eletr S., Albrecht G., Vermaas E., Williams S.R., Moon K., Burcham T., Pallas M., DuBridge R.B., Kirchner J., Fearon K., Mao J., Corcoran K. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature Biotechnology 2000, 18:630-634.
5. Chen Y., Dougherty E.R., Bittner M.L. Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics 1997, 2:364-374.
6. Cui X., Churchill G.A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biology 2003, 4:210.
7. Dharmadi Y., Gonzalez R. DNA microarrays: experimental issues, data analysis, and application to bacterial systems. Biotechnology Progress 2004, 20:1309-1324.
8. Diaconis, P., Ylvisaker, S., 1985. Quantifying prior opinion. In: Bayesian Statistics (series), vol. 2, Elsevier, New York.
9. Dudley A.M., Aach J., Steffen M.A., Church G.M. Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proceedings of the National Academy of Sciences of the United States of America 2002, 99:7554-7559.
10. Efron B., Tibshirani R., Storey J.D., Tusher V. Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 2001, 96:1151-1160.
11. Evans M., Hastings N.A.J., Peacock J.B., Hastings N.A.J. Statistical Distributions 1993, J. Wiley, New York.
12. Ghosh B. Mixture models for assessing differential expression in complex tissues using microarray data. Bioinformatics 2004, 20:1663-1669.
13. Ghosh D., Chinnaiyan A.M. Mixture modelling of gene expression data from microarray experiments. Bioinformatics 2002, 18:275-286.
14. Hoyle D.C., Rattray M., Jupp R., Brass A. Making sense of microarray data distributions. Bioinformatics 2002, 18:576-584.
15. Ji Y., Wu C.L., Liu P., Wang J., Coombes K.R. Applications of beta-mixture models in bioinformatics. Bioinformatics 2005, 21:2118-2122.
16. Kendziorski C.M., Newton M.A., Lan H., Gould M.N. On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 2003, 22:3899-3914.
17. Kerr M.K., Churchill G.A. Experimental design for gene expression microarrays. Biostatistics 2001, 2:183-201.
18. Lee M.L.T., Kuo F.C., Whitmore G.A., Sklar J. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proceedings of the National Academy of Sciences of the United States of America 2000, 97:9834-9839.
19. Mikkilineni V., Mitra R.D., Merritt J., DiTonno J.R., Church G.M., Ogunnaike B., Edwards J.S. Digital quantitative measurements of gene expression. Biotechnology and Bioengineering 2004, 86:117-124.
20. Nadon R., Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends in Genetics 2002, 18:265-271.
21. Newton M.A., Noueiry A., Sarkar D., Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 2004, 5:155-176.
22. Newton M.A., Kendziorski C.M., Richmond C.S., Blattner F.R., Tsui K.W. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 2001, 8:37-52.
23. Ozbudak E.M., Thattai M., Kurtser I., Grossman A.D., van Oudenaarden A. Regulation of noise in the expression of a single gene. Nature Genetics 2002, 31:69-73.
24. Parker R.A., Rothenberg R.B. Identifying important results from multiple statistical tests. Statistics in Medicine 1988, 7:1031-1043.
25. Quackenbush J. Computational analysis of microarray data. Nature Reviews Genetics 2001, 2:418-427.
26. Reid R.W., Fodor A.A. Determining gene expression on a single pair of microarrays. BMC Bioinformatics 2008, 9.
27. Rocke D.M., Durbin B. A model for measurement error for gene expression arrays. Journal of Computational Biology 2001, 8:557-569.
28. Sebastiani P., Gussoni E., Kohane I.S., Ramoni M.F. Statistical challenges in functional genomics. Statistical Science 2003, 18:33-60.
29. Skliar D.B., Gelmi C., Ogunnaike T., Willis B.G. Interaction of 2,2,6,6-tetramethyl-3,5-heptanedione with the Si(100)-2×1 surface: scanning tunneling microscopy and density functional theory study. Surface Science 2007, 601:2887-2895.
30. Steinhoff, C., Müller, T., Nuber, U.A., Vingron, M., 2003. Gaussian mixture density estimation applied to microarray data. In: Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science, vol. 2810, pp. 418-429.
31. Tanaka M., Earl A.M., Howell H.A., Park M.J., Eisen J.A., Peterson S.N., Battista J.R. Analysis of Deinococcus radiodurans's transcriptional response to ionizing radiation and desiccation reveals novel proteins that contribute to extreme radioresistance. Genetics 2004, 168:21-33.
32. Taylor J.R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements 1997, University Science Books, Sausalito, California.
33. Thattai M., van Oudenaarden A. Intrinsic noise in gene regulatory networks. Proceedings of the National Academy of Sciences of the United States of America 2001, 98:8614-8619.
34. Troyanskaya O.G., Garber M.E., Brown P.O., Botstein D., Altman R.B. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002, 18:1454-1461.
35. Tusher V.G., Tibshirani R., Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 2001, 98:5116-5121.
36. Wit E., McClure J.D. Statistics for Microarrays: Design, Analysis, and Inference 2004, John Wiley & Sons, Chichester, England; Hoboken, NJ, USA.