Identifying differentially expressed genes from microarray experiments via statistic synthesis

Bioinformatics

Volumn 21, Issue 7, 2005, Pages 1084-1093

  Yang, Yee Hwa ^a   Xiao, Yuanyuan ^b   Segal, Mark R ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION; GENE IDENTIFICATION; GENE MUTATION; GENETIC ALGORITHM; HUMAN; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RNA SPLICING; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

EID: 16344371755     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bti108     Document Type: Article

References (39)

1. Allison,D.B., Gadbury,G.L., Heo,M., Fernández,J.R., Lee,C.-K., Prolla,T.A. and Weindruch,R. (2002) A mixture model approach for the analysis of microarray gene expression data. Comput. Statist. Data Anal., 39, 1-20.
2. Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B, 57, 289-300.
3. Berger,J.O., Boukai,B. and Wang,Y. (1997) Unified frequentist and Bayesian testing of a precise hypothesis. Statist. Sci., 12 133-148.
4. Bowtell,D. and Sambrook,J., eds (2003) DNA Microarrays: A Molecular Cloning Manual. Cold Spring Harbor Press, Cold Spring, NY.
5. Clark,T.A., Sugnet,C.W. and Ares,M. (2002) Genomewide analysis of mRNA processing in yeast using splicing-specific microarrays. Science, 296, 907-910.
6. DeRisi,J., Penland,L., Brown,P.O., Bittner,M.L., Meltzer,P.S., Ray,M., Chen,Y., Su,Y.A. and Trent,J.M. (1996) Use ofa cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet., 14, 457-460.
7. Dudoit,S., Shaffer,J.P. and Boldrick,J.C. (2003) Multiple hypothesis testing in microarray experiments. Statist. Sci., 18 71-103.
8. Dudoit,S., Yang,YH., Callow,M.J. and Speed,T.P. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statist. Sinica, 12, 111-139.
9. Efron,B., Tibshirani,R., Goss,V. and Chu,G. (2000) Microarrays and their use in a comparative experiment. Technical Report, Department of Statistics, Stanford University, Stanford.
10. Efron,E., Tibshirani,R., Storey,J. and Tusher,V. (2001) Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Assoc., 96, 1151-1160.
11. Fleury,G., Hero,A., Yoshida,S., Carter,T., Barlow,C. and Swaroop,A. (2002) Pareto analysis for gene filtering in microarray experiments. European Signal Processing Conference.
12. Fresco,L.D. and Buratowski,S. (1996) Conditional mutants of the yeast mRNA capping enzyme show that the cap enhances, but is not required for, mRNA splicing. RNA, 2, 584-596.
13. Ge,Y. and Dudoit,S. (2002) Multiple testing procedures. R package.
14. Ghosh,D. (2003) Penalized discriminant methods for the classification of tumors from gene expression data. Biometrics, 59, 992-1000.
15. Ghosh,D. (2004) Mixture models for assessing differential expression in complex tissues using microarray data. Bioinformatics Epub ahead of print.
16. Hartzog,G.A., Wada.T., Handa,H. and Winston,F. (1998) Evidence that spt4, spt5, and spt6 control transcription elongation by RNA polymerase ii in Saccharomyces cerevisiae. Genes and Dev., 12, 357-369.
17. Huber,W., von Heydebreck,A., Sültmann,H., Poustka,A. and Vingron,M. (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 1, 1-9.
18. Ihaka,R. and Gentleman,R. (1996) R: a language for data analysis and graphics. J. Comput. Graph. Statist., 5, 299-314.
19. Irizarry,R.A., Bolstad,B.M., Collin,F., Cope,L., Hobbs,B. and Speed, T.P. (2003) Summaries of Affymetrix genechip probe level data. Nucleic Acids Res., 31, e15.
20. Jain,N., Thatte,J., Braciale,T., Ley,K., O'Connell,M. and Lee,J.K. (2003) Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics, 19, 1945-1951.
21. Jin,W., Riley,R.M., Wolfinger,R.D., White,K.P., Passador-Gurgel,G., and Gibson,G. (2001) The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat. Genet., 29, 389-395.
22. Kerr,M.K., Martin,M. and Churchill,G.A. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol., 7, 819-837.
23. Lee,M.-L.T., Kuo,F.C., Whitmore,G.A. and Sklar,J. (2000) Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl Acad. Sci. USA, 97, 9834-9839.
24. Lockhart,D.J., Dong,H.L., Byrne,M.C., Follettie,M.T., Gallo,M.V., Chee,M.S., Mittmann,M., Wang,C., Kobayashi,M. and Horton,H. (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol., 14, 1675-1680.
25. Lönnstedt,I. and Speed,T.P. (2001) Replicated microarray data. Statist. Sinica, 12, 31-46.
26. Newton,M.A., Kendziorski,C.M., Richmond,C.S., Blattner,F.R. and Tsui,K.W. (2001) On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol., 8, 37-52.
27. Newton,M.A., Noueiry,A., Sarkar,D. and Ahlquist,P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method gene expression profiles. Biostatistics, 5, 155-176.
28. Pan,W., Lin,J. and Le,C. (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol., 3, 0022.1-0022.10.
29. i-coupled receptor causes ventricular conduction delay and a lethal cardiomyopathy. Proc. Natl Acad. Sci. USA, 97, 4826-4831.
30. Rocke,D.M. and Durbin,B. (2001) A model for measurement error for gene expression arrays. J. Comput. Biol., 8, 557-570.
31. Schena,M., ed. (2000) Microarray Biochip Technology. Eaton, Natick, MA.
32. Smyth,G.K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statist. Appl. Genet. Mol. Biol., 3, Article 3, http://www.bepress.com.sagmb.
33. Speed,T.P. and Yang,Y.H. (2002) Direct and indirect hybridizations for cDNA microarray experiments. Sankhya Indian J. Statist. A, 64, 706-720.
34. Storey,J.D. (2002) A direct approach to false discovery rates. J. R. Statist. Soc. B, 64, 479-498.
35. Storey,J.D., Taylor,J.E. and Siegmund,D. (2002) A unified estimation approach to false discovery rates. Technical Report 623, Department of Statistics, University of California, Berkeley, CA.
36. Tibshirani,R., Walther,G. and Hastie,T. (2001) Estimating the number of clusters in a dataset via the gap statistic. J. R. Statist. Soc. B-Statist. Methodol., 63, 411-423.
37. Tusher,V., Tibshirani,R. and Chu,G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98, 5116.
38. Westfall,P.H. and Young,S.S. (1993) Resampling-based Multiple Testing: Examples and Methods for p-value Adjustment. John Wiley and Sons, New York.
39. Wolfinger,R.D., Gibson,G., Wolfinger,E.D., Bennett,L., Hamadeh,H., Bushel,P., Afshari,C. and Paules,R.S. (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol., 8, 625-638.