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tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities
- Source :
- BMC Bioinformatics, 15:327. BioMed Central, Miok, V, Wilting, S M, van de Wiel, M A, Jaspers, A, van Noort, P I, Brakenhoff, R H, Snijders, P J F, Steenbergen, R D M & van Wieringen, W N 2014, ' tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities ', BMC Bioinformatics, vol. 15, 327, pp. 327 . https://doi.org/10.1186/1471-2105-15-327, BMC Bioinformatics
- Publisher :
- Springer Nature
-
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. Conclusion 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. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-327) contains supplementary material, which is available to authorized users.
- Subjects :
- Keratinocytes
Semi-parametric
DNA, Complementary
Gene Dosage
Integration
Genomics
Computational biology
Biology
Empirical Bayes
Gene dosage
Biochemistry
Generalized linear mixed model
Cell Line
Bayes' theorem
Structural Biology
INLA
Humans
Computer Simulation
Copy-number variation
Gene
Molecular Biology
Regulation of gene expression
Genetics
Human papillomavirus 16
Genome
Human papillomavirus 18
Models, Genetic
Applied Mathematics
Methodology Article
Papillomavirus Infections
Bayes Theorem
DNA
High-dimensional
Computer Science Applications
Gene Expression Regulation
Host-Pathogen Interactions
DNA microarray
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 15
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....6a8fdf09d85d2f53335eadfcf9218c40
- Full Text :
- https://doi.org/10.1186/1471-2105-15-327