Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat l

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 aver...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Tung T. Nguyen, Richard R. Almon, Debra C. DuBois, William J. Jusko, Ioannis P. Androulakis
• Keywords: BMCBI 2010, Expression Profiles, Model, Transcriptional Responses