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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