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.
Keywords
DNA microarrayMicroarrayGene expressionMicroarray analysis techniquesBiologyGene expression profilingInferenceGeneExpression (computer science)Computational biologyComplementary DNAMicroarray databasesGeneticsBioinformaticsComputer scienceArtificial intelligence
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Publication Info
- Year
- 2001
- Type
- article
- Volume
- 8
- Issue
- 1
- Pages
- 37-52
- Citations
- 752
- Access
- Closed
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Cite This
Michael A. Newton,
Christina Kendziorski,
Craig S. Richmond
et al.
(2001).
On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data.
Journal of Computational Biology
, 8
(1)
, 37-52.
https://doi.org/10.1089/106652701300099074
Identifiers
- DOI
- 10.1089/106652701300099074