On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data

On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data

M.A. Newton , C.M. Kendziorski, C.S. Richmond, F.R. Blattner, and K.W. Tsui

2001, Journal of Computational Biology , 8 , 37-52.

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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.

Key words: Empirical Bayesian analysis; Global gene expression; Hierarchical modeling

(Originally issued November, 1999. Revision June 2000, Technical Report 139, Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison.)


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