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Journal of Computational Biology
On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data
To cite this article:
M.A. Newton, C.M. Kendziorski, C.S. Richmond, F.R. Blattner, K.W. Tsui. Journal of Computational Biology. February 2001, 8(1): 37-52. doi:10.1089/106652701300099074.
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M.A. Newton
Department of Statistics, University of Wisconsin, Madison, WI 53792; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792.
C.M. Kendziorski
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI 53792.
C.S. Richmond
Laboratory of Genetics, University of Wisconsin, Madison, WI 53792.
F.R. Blattner
Laboratory of Genetics, University of Wisconsin, Madison, WI 53792.
K.W. Tsui
Department of Statistics, University of Wisconsin, Madison, WI 53792.

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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Authors:
M.A. Newton
C.M. Kendziorski
C.S. Richmond
F.R. Blattner
K.W. Tsui
Keywords:
EMPIRICAL BAYESIAN ANALYSIS
GLOBAL GENE EXPRESSION
HIERARCHICAL MODELING
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