Distribution modeling and simulation of gene expression data

Computational Statistics and Data Analysis

Volumn 53, Issue 5, 2009, Pages 1650-1660

  Parrish, Rudolph S ^a   Spencer III, Horace J ^b   Xu, Ping ^a  

^a UNIVERSITY OF LOUISVILLE   (United States)

^b UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOACTIVITY; BIOASSAY; FOURIER TRANSFORMS; GENE EXPRESSION; GENES; MATHEMATICAL TRANSFORMATIONS; MICROARRAYS; PARAMETER ESTIMATION; PROBES;

CHOLESKY; DATA ANALYSIS METHODS; DATA NORMALIZATIONS; GAUSSIAN; GENE EXPRESSION DATUM; GENE EXPRESSION MICROARRAYS; INTENSITY DATUM; MATRIX FACTORIZATIONS; MODELING AND SIMULATIONS; MULTIVARIATE MODELS; MULTIVARIATE NORMAL DISTRIBUTIONS; PROSTATE CANCERS; SINGULARITY PROBLEMS; STATISTICAL MODELS; STATISTICAL PROPERTIES; TRANSFORMED VARIABLES;

NORMAL DISTRIBUTION;

EID: 60349126054     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.03.023     Document Type: Article

References (34)

1. Affymetrix. Statistical Algorithms Description Document (2002), Affymetrix, Inc., Santa Clara, CA. http://www.affymetrix.com/support/technical/whitepapers/sadd-whitepaper.pdf
2. Albers C.J., Jansen R.C., Kok J., Kuipers O.P., and van Hijum S. SIMAGE: Simulation of DNA-microarray gene expression data. BMC Bioinformatics 7 (2006) 205
3. Allison D.B., Gadbury G.L., Heo M., Fernandez J.R., Lee C.-K., Prolla A., and Weindruch R. A mixture model approach for the analysis of microarray gene expression data. Computational Statistics and Data Analysis 39 (2002) 1-20
4. Bioconductor, 2003. Methods for Affymetrix Oligonucleotide Arrays (affy), Bioconductor Project, Version 1.2. http://www.bioconductor.org/
5. U distributions fitted by percentiles: A general criterion. Communications in Statistics, Part B-Simulation and Computation 18 (1989) 1-13
6. Box G.E.P., and Cox D.R. The analysis of transformations (with discussion). Journal of the Royal Statistical Society B 26 (1964) 211-252
7. Giles P.J., and Kipling D. Normality of oligonucleotide microarray data and implications for parametric statistical analyses. Bioinformatics 19 17 (2003) 2254-2262
8. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer, New York pp. 502-504
9. Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U., and Speed T.P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4 (2003) 249-264
10. Johnson M.E. Multivariate Statistical Simulation: A Guide to Selecting and Generating Continuous Multivariate Distributions (1987), John Wiley & Sons, New York
11. Johnson N.L. Systems of frequency curves generated by methods of translation. Biometrika 36 (1949) 149-176
12. U frequency curves. Biometrika 52 (1965) 547-558
13. B curves. Biometrika 58 (1971) 223-226
14. Johnson N.L., and Kotz S. Distributions in Statistics: Continuous Univariate Distributions vol. 1 (1970), Houghton-Mifflin Company, Boston
15. Johnson N.L., and Kotz S. Distributions in Statistics: Continuous Multivariate Distributions (1972), John Wiley and Sons, New York
16. Kennedy W.J., and Gentle J.E. Statistical Computing (1980), Marcel-Dekker, New York
17. 2. Journal of Statistical Computation and Simulation 75 3 (2005) 199-206
18. Li C., and Wong W.H. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Sciences USA 98 (2001) 31-36
19. Molinaro A.M., Simon R., and Pfeiffer R.M. Prediction error estimation: A comparison of resampling methods. Bioinformatics 21 15 (2005) 3301-3307
20. Morrison D.F. Multivariate Statistical Methods. 2nd edition (1976), McGraw-Hill, New York
21. Nguyen D.V., Arpat A.B., Wang N., and Carroll R.J. DNA microarray experiments: Biological and technological aspects. Biometrics 58 (2002) 701-717
22. Nykter M., Aho T., Ahdesmaki P., Lehmussola A., and Yli-Harja O. Simulation of microarray data with realistic characteristics. BMC Bioinformatics 7 (2006) 349
23. Rose C., and Smith M.D. Mathematical Statistics with Mathematica (2002), Springer, New York
24. Sakia R.M. The Box-Cox transformation technique: A review. The Statistician 41 (1992) 169-178
25. Shayib M.A. The procedure for selection of transformations from the Johnson system. Communications in Statistics, Part B-Simulation and Computation 18 (1989) 1457-1464
26. Singh D., Febbo P.G., Ross K., et al. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203-209
27. Singhal S., Kyvernitis C.G., Johnson S.W., Kaisera L.R., Liebman M.N., and Albelda S.M. Microarray data simulator for improved selection of differentially expressed genes. Cancer Biology and Therapeutics 2 (2003) 383-391
28. Slifker J.F., and Shapiro S.S. The Johnson system: Selection and parameter estimation. Technometrics 22 (1980) 239-246
29. Schnabel R.B., and Eskow E. A new modified Cholesky factorization. SIAM Journal of Scientific Statistical Computing 11 (1990) 1136-1158
30. Schnabel R.B., and Eskow E. Algorithm 695: Software for a new modified Cholesky factorization. Transactions on Mathematical Software 17 3 (1991) 306-312
31. Schnabel R.B., and Eskow E. A revised modified Cholesky factorization algorithm. SIAM Journal on Optimization 9 4 (1999) 1135-1148
32. Wheeler R.E. Quantile estimators of Johnson curve parameters. Biometrika 67 (1980) 725-728
33. Xu P., Brock G.N., and Parrish R.S. Modified linear discriminant analysis approaches for classification of high-dimensional microarray data. Computational Statistics and Data Analysis 53 5 (2009) 1674-1687
34. Zhou B., and McTague J.P. Comparison and evaluation of five methods of estimation of the Johnson system parameters. Canadian Journal of Forest Research 26 (1996) 928-935