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Summary: METHODOLOGY ARTICLE Open Access
Importance of replication in analyzing time-series
gene expression data: Corticosteroid dynamics
and circadian patterns in rat liver
Tung T Nguyen1
, Richard R Almon4,5,6
, Debra C DuBois4,5
, William J Jusko4,6
, Ioannis P Androulakis2,3*
Abstract
Background: Microarray technology is a powerful and widely accepted experimental technique in molecular
biology that allows studying genome wide transcriptional responses. However, experimental data usually contain
potential sources of uncertainty and thus many experiments are now designed with repeated measurements to
better assess such inherent variability. Many computational methods have been proposed to account for the
variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information
so that a variety of computational models that take the expression profiles as the input data can explore this
information without any modification.
Results: We propose a methodology which integrates replicate variability into expression profiles, to generate
so-called `true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate
`true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-
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