A discussion of statistical methods for design and analysis of microarray experiments for plant scientists

Plant Cell

Volumn 18, Issue 9, 2006, Pages 2112-2121

  Nettleton, Dan ^a  

^a Department of Statistics Center for Statistics and Applications in Forensic Evidence Iowa State University 2438 Osborn Dr Ames   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33749260517     PISSN: 10404651     EISSN: None     Source Type: Journal    
DOI: 10.1105/tpc.106.041616     Document Type: Review

References (32)

1. Allison, D.B., Cui, X., Page, G.P., and Sabripour, M. (2006). Microarray data analysis: From disarray to consolidation and consensus. Nat. Rev. Genet. 7, 55-65.
2. Altman, N.S. (2005). Replication, variation and normalization in microarray experiments. Appl. Bioinformatics 4, 33-44.
3. Baldi, P., and Long, A. (2001). A Bayesian framework for the analysis inferences of gene changes. Bioinformatics 17, 509-519.
4. Barry, W.T., Nobel, A.B., and Wright, F.A. (2005). Significance analysis of functional categories in gene expression studies: A structured permutation approach. Bioinformatics 21, 1943-1949.
5. Benjamini, Y., and Hochberg, Y. (1995). Controlling false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B 57, 289-300.
6. Caldo, R.A., Nettleton, D., and Wise, R.P. (2004). Interaction-dependent gene expression in M/a-specified response to barley powdery mildew. Plant Cell 16, 2514-2528.
7. Churchill, G.A. (2002). Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32 (suppl.), 490-495.
8. Cui, X., Hwang, J.T.G., Qiu, J., Blades, N.J., and Churchill, G.A. (2005). Improved statistical tests for differential gene expression by shrinking variance component estimates. Biostatistics 6, 59-75.
9. DeCook, R., Lall, S., Nettleton, D., and Howell, S.H. (2006). Genetic regulation of gene expression during shoot development in Arabidopsis. Genetics 172, 1155-1164.
10. Dobbin, K., Shih, J.H., and Simon, R. (2003). Statistical design of reverse dye microarrays. Bioinformatics 19, 803-810.
11. Fisher, R.A. (1951). Design of Experiments, 6th ed. (Edinburgh, UK: Oliver and Boyd).
12. Glonek, G.F.V., and Solomon, P.J. (2004). Factorial and time course designs for cDNA microarray experiments. Biostatistics 5, 89-111.
13. Kendziorski, C., Irizarry, R.A., Chen, K.-S., Haag, J.D., and Gould, M.N. (2005). On the utility of pooling biological samples in microarray experiments. Proc. Natl. Acad. Sci. USA 102, 4252-4257.
14. Kendziorski, C.M., Newton, M.A., Lan, H., and Gould, M.N. (2003a). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Stat. Med. 22, 3899-3914.
15. Kendziorski, C.M., Zhang, Y., Lan, H., and Attie, A.D. (2003b). The efficiency of mRNA pooling in microarray experiments. Biostatistics 4, 465-477.
16. Kerr, M.K. (2003). Design considerations for efficient and effective microarray studies. Biometrics 59, 822-828.
17. Kerr, M.K., and Churchill, G.A. (2001a). Experimental design for gene expression microarrays. Biostatistics 2, 183-201.
18. Kerr, M.K., and Churchill, G.A. (2001b). Statistical design and the analysis of gene expression microarrays. Genet. Res. 77, 123-128.
19. Kerr, M.K., Martin, M., and Churchill, G.A. (2000). Analysis of variance for gene expression microarray data. J. Comput. Biol. 7, 819-837.
20. Lall, S., Nettleton, D., DeCook, R., Che, P., and Howell, S.H. (2004). QTLs associated with adventitious shoot formation in tissue culture and the program of shoot development in Arabidopsis. Genetics 167, 1883-1892.
21. Langaas, M., Lindqvist, B., and Ferkingstad, E. (2005). Estimating the proportion of true null hypotheses, with application to DNA microarray data. J. R. Stat. Soc. Series B 67, 555-572.
22. McCulloch, C.E., and Searle, S.R. (2001). Generalized, Linear, and Mixed Models. (New York: John Wiley & Sons).
23. Smyth, G.K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3.
24. Storey, J.D., and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440-9445.
25. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and Mesirov, J.P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550.
26. Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116-5121.
27. Vanneste, S., et al. (2005). Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana. Plant Cell 17, 3035-3050.
28. Vuylsteke, M., van Eeuwijk, F., Van Hummelen, P., Kuiper, M., and Zabeau, M. (2005). Genetic analysis of variation in gene expression in Arabidopsis thaliana. Genetics 171, 1267-1275.
29. 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, 625-637.
30. Wright, G.W., and Simon, R.M. (2003). A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 19, 2448-2455.
31. Wright, G.W., and Simon, R.M. (2003). A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 19, 2448-2455.
32. Yang, Y.H., and Speed, T.P. (2002). Design issues for cDNA microarray experiments. Nat. Rev. Genet. 3, 579-588.