Reassessing design and analysis of two-colour microarray experiments using mixed effects models

Comparative and Functional Genomics

Volumn 6, Issue 3, 2005, Pages 123-131

  Rosa, Guilherme J M ^a   Steibel, Juan P ^a   Tempelman, Robert J ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Experimental design; Group comparison; Hierarchial replication; Microarray; Mixed models

Indexed keywords

GENOMIC DNA;

ANALYSIS OF VARIANCE; ANALYTICAL ERROR; ARTICLE; CALCULATION; COLORIMETRY; DATA ANALYSIS; DNA MICROARRAY; EXPERIMENTAL DESIGN; LINEAR SYSTEM; METHODOLOGY; PRIORITY JOURNAL; PUBLICATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 18844414500     PISSN: 15316912     EISSN: None     Source Type: Journal    
DOI: 10.1002/cfg.464     Document Type: Article

References (22)

1. Black MA, Doerge RW. 2002. Calculation of the minimum number of replicate spots required for detection of significant expression fold change in microarray experiments. Bioinformatics 18: 1609-1616.
2. Churchill GA. 2002. Fundamentals of experimental design for cDNA microarrays. Nature Genet 32: 490-495.
3. Cui X, Churchill GA. 2003. Methods in microarray data analysis III. In Methods of Microarray Data Analysis III: Papers from Camda '02, Johnson KF, Lin SM (eds). Kluwer Academic: Dordrecht.
4. Dudoit S, Shaffer JP, Boldrick JC. 2003. Multiple hypothesis testing in microarray experiments. Statist Sci 18: 71-103.
5. Dobbin K, Simon R. 2002. Comparison of microarray designs for class comparison and class discovery. Bioinformatics 18: 1438-1445.
6. Dobbin K, Shih JH, Simon R. 2003. Statistical design of reverse dye microarrays. Bioinformatics 19: 803-810.
7. Feng S, Wolfinger RD, Chu TM, Gibson GC, McGraw LA. 2005. Empirical Bayesian analysis of variance component models for microarray data. J Agric Biol Environ Stat (in press).
8. Gadbury GL, Page GP, Edwards J, et al. 2004. Power and sample size estimation in high dimensional biology. Statist Methods Med Res 13: 325-338.
9. Gill JL. 1978. Design and Analysis of Experiments in the Animal and Medical Sciences, vol I. Iowa State University Press: Ames, IA.
10. Kerr MK. 2003. Design considerations for efficient and effective microarray studies. Biometrics 59: 822-828.
11. Kerr MK, Churchill GA. 2001a. Experimental design for gene expression microarrays. Biostatistics 2: 183-201.
12. Kerr MK, Churchill GA. 2001b. Statistical design and the analysis of gene expression microarray data. Genet Res 77: 123-128.
13. Kerr MK, Martin M, Churchill GA. 2000. Analysis of variance for gene expression microarray data. J Comp Biol 7: 819-837.
14. Lee M-LT, Whitmore GA. 2002. Power and sample size for DNA microarray studies. Statist Med 21: 3543-3570.
15. Leil TA, Ossaditchi A, Nichols TE, Leahey RM, Smith D. 2003. Genes regulated by learning in the hippocampus. J Neuroci Res 71: 763-768.
16. Pavlidis P, Li QH, Noble WS. 2003. The effect of replication on gene expression microarray experiments. Bioinformatics 19: 1620-1627.
17. Steibel JP, Tempelman RJ, Rosa GJM. 2005. Power and sample size determinations for two colour microarray experiments based on different levels of replication (submitted for publication).
18. Stroup WW. 2002. Power analysis based on spatial effects mixed models: a tool for comparing design and analysis strategies in the presence of spatial variability. J Agric Biol Environ Statist 7: 491-511.
19. Tempelman RJ. 2005. Assessing statistical precision, power, and robustness of alternative experimental designs for two colour microarray platforms based on mixed effects models. Vet Immunol Immunopathol (in press).
20. Wernisch L, Kendall SL, Soneji S, et al. 2003. Analysis of whole-genome microarray replicates using mixed models. Bioinformatics 19: 53-61.
21. Wolfinger RD, Gibson G, Wolfinger ED, et al. 2001. Assessing gene significance from cDNA microarray expression data via mixed models. J Comp Biol 8: 625-637.
22. Yang YH, Speed T. 2002. Design issues for cDNA microarray experiments. Nature Rev Genet 3: 579-588.