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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

This plot illustrates the effect of using different priors in one cell line. Gene expression is plotted against time (single cell line only). The solid red line is the fit of the model with a standard prior, while the dashed blue line is that of the model with an alternative prior.
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Fig7: This plot illustrates the effect of using different priors in one cell line. Gene expression is plotted against time (single cell line only). The solid red line is the fit of the model with a standard prior, while the dashed blue line is that of the model with an alternative prior.

Mentions: Finally, we want to assess the sensitivity of the results with respect to the choice of the prior distribution. For illustration purposes we focus on the hyperprior of the random effect γk,j. In the Section ‘Estimation’ we suggest to use the Gamma distribution. However, we have also implemented a mixture of a point mass at zero and Gamma distribution, which one expects to lead to more shrinkage. Model (2) using different splines and a standard design matrix is refitted now with this mixture prior for the random spline effect. Application of our empirical Bayes procedure with the Gamma prior identified 421 features, while the Dirac-Gamma mixture prior selected 396 features. The latter 396 are all included in the former 421 features. The slight reduction in the number of selected features is of course due to the inclusion of the point mass at zero. The fit of both resulting models is almost identical for most features, but for some features with a slightly less flexible spline as in Figure 7 (Additional file 1, Section 7 illustrates effect in all four cell lines).Figure 7


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)

This plot illustrates the effect of using different priors in one cell line. Gene expression is plotted against time (single cell line only). The solid red line is the fit of the model with a standard prior, while the dashed blue line is that of the model with an alternative prior.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig7: This plot illustrates the effect of using different priors in one cell line. Gene expression is plotted against time (single cell line only). The solid red line is the fit of the model with a standard prior, while the dashed blue line is that of the model with an alternative prior.
Mentions: Finally, we want to assess the sensitivity of the results with respect to the choice of the prior distribution. For illustration purposes we focus on the hyperprior of the random effect γk,j. In the Section ‘Estimation’ we suggest to use the Gamma distribution. However, we have also implemented a mixture of a point mass at zero and Gamma distribution, which one expects to lead to more shrinkage. Model (2) using different splines and a standard design matrix is refitted now with this mixture prior for the random spline effect. Application of our empirical Bayes procedure with the Gamma prior identified 421 features, while the Dirac-Gamma mixture prior selected 396 features. The latter 396 are all included in the former 421 features. The slight reduction in the number of selected features is of course due to the inclusion of the point mass at zero. The fit of both resulting models is almost identical for most features, but for some features with a slightly less flexible spline as in Figure 7 (Additional file 1, Section 7 illustrates effect in all four cell lines).Figure 7

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