title: Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver.
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-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.
  
  
  CONCLUSIONS
  We 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.
authors.affiliation: BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.
authors.forename: Tung T
authors.initials: TT
authors.lastname: Nguyen
authors.forename: Richard R
authors.initials: RR
authors.lastname: Almon
authors.forename: Debra C
authors.initials: DC
authors.lastname: DuBois
authors.forename: William J
authors.initials: WJ
authors.lastname: Jusko
authors.forename: Ioannis P
authors.initials: IP
authors.lastname: Androulakis
mesh_terms: D000305:Adrenal Cortex Hormones / Q000378:metabolism*; D000818:Animals; D003198:Computer Simulation; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D008099:Liver / Q000378:metabolism*; D056950:Period Circadian Proteins / Q000235:genetics*; D051381:Rats; D014158:Transcription, Genetic
chemical_list: D000305:Adrenal Cortex Hormones; D056950:Period Circadian Proteins