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tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities.

Miok V, Wilting SM, van de Wiel MA, Jaspers A, van Noort PI, Brakenhoff RH, Snijders PJ, Steenbergen RD, van Wieringen WN - BMC Bioinformatics (2014)

Bottom Line: 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.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.Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

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.

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Related in: MedlinePlus

Illustration of the same and different spline model. Each panel, one per cell line, plots gene expression against time. The solid red curve represents the fit of the model with a different spline per cell line, while the dashed blue lines depict the fit of the same spline model.
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Fig1: Illustration of the same and different spline model. Each panel, one per cell line, plots gene expression against time. The solid red curve represents the fit of the model with a different spline per cell line, while the dashed blue lines depict the fit of the same spline model.

Mentions: A straightforward extension of Model (2) is to allow for a different spline in each cell line. This reflects the biological plausibility of different dynamical behaviour in different cell lines. In particular, we may then test for differences in the behavior over time between the cell lines. When rewriting Model (2) to a vector notation, the incorporation of different splines per cell line amounts to the replacement of by (the operator ⊗ is the Kronecker product), and adjusting the parameter vector γj accordingly. Figure 1 illustrates the difference between the same and different spline models.Figure 1


tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities.

Miok V, Wilting SM, van de Wiel MA, Jaspers A, van Noort PI, Brakenhoff RH, Snijders PJ, Steenbergen RD, van Wieringen WN - BMC Bioinformatics (2014)

Illustration of the same and different spline model. Each panel, one per cell line, plots gene expression against time. The solid red curve represents the fit of the model with a different spline per cell line, while the dashed blue lines depict the fit of the same spline model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4288633&req=5

Fig1: Illustration of the same and different spline model. Each panel, one per cell line, plots gene expression against time. The solid red curve represents the fit of the model with a different spline per cell line, while the dashed blue lines depict the fit of the same spline model.
Mentions: A straightforward extension of Model (2) is to allow for a different spline in each cell line. This reflects the biological plausibility of different dynamical behaviour in different cell lines. In particular, we may then test for differences in the behavior over time between the cell lines. When rewriting Model (2) to a vector notation, the incorporation of different splines per cell line amounts to the replacement of by (the operator ⊗ is the Kronecker product), and adjusting the parameter vector γj accordingly. Figure 1 illustrates the difference between the same and different spline models.Figure 1

Bottom Line: 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.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.Furthermore, the proposed method yields improvements in sensitivity, specificity and reproducibility compared to existing methods.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P,O, Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

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.

Show MeSH
Related in: MedlinePlus