The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data
Abstract
We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured fluorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and fluctuations in absolute gene expression levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Cited by
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440
Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis
Chang, T.-C., Wentzel, E. A., Kent, O. A., Ramachandran, K., Mullendore, M., Lee, K. H., Feldmann, G., Yamakuchi, M., Ferlito, M., Lowenstein, C. J., et al. (2007) · Molecular Cell · doi:10.1016/j.molcel.2007.05.010 · Cited by 2,010
Metadata
- Authors
- Newton, M. A., Kendziorski, C. M., Richmond, C. S., Blattner, F. R., & Tsui, K. W.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 2001-02
- Volume
- 8
- Issue
- 1
- Pages
- 37-52
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W1995017987
- Cited by
- 739
- References
- 2
- FWCI
- 23.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Newton, M. A., Kendziorski, C. M., Richmond, C. S., Blattner, F. R., & Tsui, K. W. (2001). On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data. Journal of Computational Biology. https://doi.org/10.1089/106652701300099074
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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