The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A random variance model for detection of differential gene expression in small microarray experiments
Abstract
Motivation: Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. Results: We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. Availability: This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). Supplementary material: ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F., Ben Miled, S., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,803
Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R. I., Gascoyne, R. D., Muller-Hermelink, H. K., Smeland, E. B., Giltnane, J. M., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa012914 · Cited by 3,829
Self-citationGene-Expression Profiles in Hereditary Breast Cancer
Hedenfalk, I., Duggan, D., Chen, Y., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Raffeld, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200102223440801 · Cited by 1,733
Self-citationBaldi, P., & Long, A. D. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.509 · Cited by 1,618
Experimental design for gene expression microarrays
Kerr, M. K. (2001) · Biostatistics · doi:10.1093/biostatistics/2.2.183 · Cited by 706
Controlling the number of false discoveries: application to high-dimensional genomic data
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voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
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McCarthy, D. J., & Smyth, G. K. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp053 · Cited by 721
Yang, F., Zhang, L., Huo, X.-S., Yuan, J.-H., Xu, D., Yuan, S.-X., Zhu, N., Zhou, W.-P., Yang, G.-S., Wang, Y.-Z., et al. (2011) · Hepatology · doi:10.1002/hep.24563 · Cited by 632
Metadata
- Authors
- Wright, G. W., & Simon, R. M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-12-12
- Volume
- 19
- Issue
- 18
- Pages
- 2448-2455
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2116815663
- Cited by
- 665
- References
- 7
- FWCI
- 6.65
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
