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Statistical design and the analysis of gene expression microarray data
Open accessAbstract
Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue that classical statistical techniques are appropriate and useful. We advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.
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Cited by
Fundamentals of experimental design for cDNA microarrays
Churchill, G. A. (2002) · Nature Genetics · doi:10.1038/ng1031 · Cited by 1,050
Variation in gene expression within and among natural populations
Oleksiak, M. F., Churchill, G. A., & Crawford, D. L. (2002) · Nature Genetics · doi:10.1038/ng983 · Cited by 736
Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments
Kerr, M. K., & Churchill, G. A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.161273698 · Cited by 474
Transcriptional outputs of the Caenorhabditis elegans forkhead protein DAF‐16
McElwee, J., Bubb, K., & Thomas, J. H. (2003) · Aging Cell · doi:10.1046/j.1474-9728.2003.00043.x · Cited by 416
Statistical Design and Analysis of RNA Sequencing Data
Auer, P. L., & Doerge, R. W. (2010) · Genetics · doi:10.1534/genetics.110.114983 · Cited by 393
Metadata
- Authors
- KATHLEEN KERR, M., & A. CHURCHILL, G.
- Venue
- Genetical Research
- Publisher
- Hindawi Limited
- Published
- 2001-02
- Volume
- 77
- Issue
- 2
- Pages
- 123-128
- ISSN
- 0016-6723 (ISSN-L)
- 1469-5073
- OpenAlex
- W4239901699
- Cited by
- 483
- References
- 0
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- bronze
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