Abstract
Microarrays provide the ability to quantitatively measure the abundance of specific RNA transcripts through sample hybridization to a solid-state grid of oligonucleotides or amplicons. The prospect of measuring the entire transcriptome is extremely alluring, but as with any experiment, it should be met with caution and great consideration. The level of confidence we can assign to the results depends on the skill at which the experiment is conducted, the quality of the experimental design and subsequent analysis, and, most important, the power in the study. Any microarray experiment consists of several components: (1) carrying out an appropriately designed (replicated) plant experiment; (2) array processing, which includes several steps of data acquisition and normalization; and (3) analysis of expression data to identify differentially expressed genes and overall patterns of expression. Numerous software packages are available to assist in performing these steps and it is not our intent to provide a software users manual or a statistical review. It is our intent to provide a brief user’s explanation of these various components and present the commonly used methods.
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References
Harmer, S. L., Hogenesch, J. B., Straume, M., et al. (2000) Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science 290, 2110–2113.
Yang, Y. H. and Speed, T. (2002) Design issues for cDNA microarray experiments. Nat. Rev. Genet. 3, 579–588.
Churchill, G. A. (2002) Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32 Suppl, 490–495.
Kerr, M. K. and Churchill, G. A. (2001) Statistical design and the analysis of gene expression microarray data. Genet. Res. 77, 123–128.
Cui, X. and Churchill, G. A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4, 210.
Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300.
Cope, L. M., Irizarry, R. A., Jaffee, H. A., Wu, Z., and Speed, T. P. (2004) A benchmark for Affymetrix GeneChip expression measures. Bioinformatics. 20, 323–331.
Li, C. and Wong, W. H. (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36.
Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B., and Speed, T. P. (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15.
Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J., and Speed, T. P. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15.
Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet. 32 Suppl, 496–501.
Bolstad, B. M., Irizarry, R. A., Astrand, M., and Speed, T. P. (2003) A comparison of normalizationmethods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193.
Astrand, M. (2003) Contrast normalization of oligonucleotide arrays. J. Comput. Biol. 10, 95–102.
Schadt, E. E., Li, C., Ellis, B. and Wong, W. H. (2001) Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J. Cell Biochem. Suppl. Suppl 37, 120–125.
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.
Kerr, M. K., Martin, M., and Churchill, G. A. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol. 7, 819–837.
Wolfinger, R. D., Gibson, G., Wolfinger, E. D., et al. (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8, 625–637.
Smyth, G. K., Yang, Y. H., and Speed, T. (2003) Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224, 111–136.
Yeung, K. Y., Haynor, D. R., and Ruzzo, W. L. (2001) Validating clustering for gene expression data. Bioinformatics 17, 309–318.
Eisen, M. B., Spellman, P. T., Brown, P. O., and Botstein, D (1998). Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14,863–14,868.
Kerr, M. K. and Churchill, G. A. (2001) Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc. Natl. Acad. Sci. USA 98, 8961–8965.
Gasch, A. P. and Eisen, M. B. (2002) Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 3, RESEARCH0059.
Tamayo, P., Slonim, D., Mesirov, J., et al. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912.
Brazma, A. and Vilo, J. (2000) Gene expression data analysis. FEBS Lett. 480, 17–24.
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Rensink, W.A., Hazen, S.P. (2006). Statistical Issues in Microarray Data Analysis. In: Salinas, J., Sanchez-Serrano, J.J. (eds) Arabidopsis Protocols. Methods in Molecular Biology™, vol 323. Humana Press. https://doi.org/10.1385/1-59745-003-0:359
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DOI: https://doi.org/10.1385/1-59745-003-0:359
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