Comparing three methods for variance estimation with duplicated high density oligonucleotide arrays

Functional and Integrative Genomics

Volumn 2, Issue 3, 2002, Pages 126-133

  Huang, Xiaohong ^a   Pan, Wei ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Differential gene expression; Microarray; Nonparametric smoothing

Indexed keywords

ANALYTICAL ERROR; ARTICLE; CLINICAL ARTICLE; COLON TUMOR; CONTROLLED STUDY; DNA MICROARRAY; GENE DUPLICATION; GENE EXPRESSION; GENE IDENTIFICATION; GENETIC VARIABILITY; HUMAN; HUMAN TISSUE; NONPARAMETRIC TEST; PRIORITY JOURNAL; STATISTICAL ANALYSIS; ALGORITHM; COMPARATIVE STUDY; EVALUATION; GENE EXPRESSION PROFILING; GENETICS; REPRODUCIBILITY;

ALGORITHMS; COLONIC NEOPLASMS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 0036690521     PISSN: 1438793X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10142-002-0066-2     Document Type: Article

References (26)

1. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci 96: 6745-6750
2. Baggerly KA, Coombes KR, Hess KR, Stivers DN, Abruzzo LV, Zhang W (2001) Identifying differentially expressed genes in cDNA microarray experiments. J Comput Biol 8:639-659
3. Baldi P, Long AD (2001) A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17:509-519
4. Brown P, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet Suppl 21:33-37
5. Chen Y, Dougherty ER, Bittner ML (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364-367
6. Cleveland W, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596-610
7. Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 12:111-139
8. Efron B, Tibshirani R, Storey JD, Tusher V (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151-1160
9. Ideker T, Thorsson V, Siehel AF, Hood LE (2000) Testing for differentially-expressed genes by maximum likelihood analysis of microarray data. J Comput Biol 7:805-817
10. Kamb A, Ramaswami M (2001) A simple method for statistical analysis of intensity differences in microarray-derived gene expression data. Biotechnology 1:8
11. Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. J Comput Biol 7:819-837
12. Lander ES (1999) Array of hope. Nat Genet Suppl 21:3-4
13. Lee M-LT, Kuo FC, Whitmore GA, Sklar J (2000) Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci 97:9834-9839
14. Li H, Hong F (2001) Cluster-Rasch models for microarray gene expression data. Genome Biol 2(8):0031.1-0031.13
15. Li C, Wong WH (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci 98:31-36
16. Lin Y, Nadler ST, Attie AD, Yandell BS (2001) Mining for low-abundance transcripts in microarray data. Available at http://www.stat.wisc.edu/~ yilin/
17. Lipshutz RJ, Fodor SPA, Gineras TR, Lockhart DJ (1999) High density synthetic oligonucleotide arrays. Nat Genet Suppl 21:20-24
18. Neter J, Kutner MH, Nachtsheim JC, Wasserman W (1996) Applied linear statistical models. WCB/McGraw-Hill, Maiden-head, pp 425-427
19. Newton MA, Kendziorski CM, Richmond CS, Blattner FR, Tsui KW (2001) On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol 8:37-52
20. Pan W (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 12:546-554. Available at http://www.biostat.umn.edu/cgi- bin/rrs?print+2001
21. Pan W, Lin J, Le C (2001) A mixture model approach to detecting differentially expressed genes with microarray data. Technical Report. Division of Biostatistics, University of Minnesota. Available at http://www.biostat.umn. edu/cgi-bin/rrs?print+2001
22. Pan W, Lin J, Le C (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol /2002/3/5/research/0022
23. Rocke DM, Durbin B (2001) A model for measurement error for gene expression arrays. J Comput Biol 8:557-570
24. Thomas JG, Olson JM, Tapscott SJ, Zhao LP (2001) An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 11:1227-1236
25. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 98:5116-5121
26. Wolfinger R, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 8:625-638