title: tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities.
abstract: BACKGROUND
  To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell lines affected with either HPV16 or HPV18 were assayed at 8 sequential time points for gene expression (mRNA) and gene copy number (DNA) using high-resolution microarrays. Available methods for temporal differential expression analysis are not designed for integrative genomic studies.
  
  
  RESULTS
  Here, we present a method that allows for the identification of differential gene expression associated with DNA copy number changes over time. The temporal variation in gene expression is described by a generalized linear mixed model employing low-rank thin-plate splines. Model parameters are estimated with an empirical Bayes procedure, which exploits integrated nested Laplace approximation for fast computation. Iteratively, posteriors of hyperparameters and model parameters are estimated. The empirical Bayes procedure shrinks multiple dispersion-related parameters. Shrinkage leads to more stable estimates of the model parameters, better control of false positives and improvement of reproducibility. In addition, to make estimates of the DNA copy number more stable, model parameters are also estimated in a multivariate way using triplets of features, imposing a spatial prior for the copy number effect.
  
  
  CONCLUSIONS
  With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by DNA copy number abnormalities. In particular, in the analysis of an integrative oncogenomics study with a time-course set-up our method finds genes previously reported to be involved in cervical carcinogenesis. Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods. Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.
authors.forename: Viktorian
authors.initials: V
authors.lastname: Miok
authors.forename: Saskia M
authors.initials: SM
authors.lastname: Wilting
authors.forename: Mark A
authors.initials: MA
authors.lastname: van de Wiel
authors.forename: Annelieke
authors.initials: A
authors.lastname: Jaspers
authors.forename: Paula I
authors.initials: PI
authors.lastname: van Noort
authors.forename: Ruud H
authors.initials: RH
authors.lastname: Brakenhoff
authors.forename: Peter J F
authors.initials: PJ
authors.lastname: Snijders
authors.forename: Renske D M
authors.initials: RD
authors.lastname: Steenbergen
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.
authors.forename: Wessel N
authors.initials: WN
authors.lastname: van Wieringen
mesh_terms: D001499:Bayes Theorem; D002460:Cell Line; D003198:Computer Simulation; D004247:DNA / Q000235:genetics; D018076:DNA, Complementary; D018628:Gene Dosage; D005786:Gene Expression Regulation; D016678:Genome; D023281:Genomics / Q000379:methods*; D054884:Host-Pathogen Interactions; D052162:Human papillomavirus 16 / Q000502:physiology*; D052161:Human papillomavirus 18 / Q000502:physiology*; D006801:Humans; D015603:Keratinocytes / Q000378:metabolism / Q000821:virology*; D008957:Models, Genetic; D030361:Papillomavirus Infections / Q000235:genetics
chemical_list: D018076:DNA, Complementary; D004247:DNA