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

BMC Bioinformatics

Methodology Article

Tung T Nguyen¹  Ioannis P Androulakis²  Debra C DuBois³  Richard R Almon⁴  William J Jusko⁵ 

[1] BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA;Biomedical Engineering Department, Rutgers University, Piscataway, New Jersey, USA;Chemical & Biochemical Engineering Department, Rutgers University, Piscataway, New Jersey, USA;Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA;Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, USA;Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA;Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York, USA;New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA;Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA;New York State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA;

关键词: Synthetic Data;    Transcriptional Response;    Cluster Performance;    Similar Expression Pattern;    Adjusted Rand Index;   

DOI  :  10.1186/1471-2105-11-279

 received in 2009-10-08, accepted in 2010-05-26,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMicroarray 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.ResultsWe 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-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze in vivo rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.ConclusionsWe have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.

【 授权许可】

Unknown   
© Nguyen et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.