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

Abstract

BackgroundTo 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.ResultsHere, 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.ConclusionWith 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 materialThe online version of this article (doi:10.1186/1471-2105-15-327) contains supplementary material, which is available to authorized users.

Identification of Deregulated Pathways, Key Regulators, and Novel miRNA-mRNA Interactions in HPV-Mediated Transformation

“…Besides studying the effect of copy number aberrations on gene expression, the tigaR framework also proved suitable for the identification of temporal miRNA-mRNA interactions [17]. Opposed to BRWD3 and PLXNB2 being downregulated upon overexpression of their validated miRNA interaction partner, FOS expression increased upon miR-221-3p overexpression.…”

“…Even though there was no change in PCNA levels upon PITX2 overexpression, increased levels of CDKN1B and cleaved CASP3 suggested that reduced cell viability in PITX2 overexpressing cells is associated to both cell cycle arrest and apoptosis ( Figure 3d). method, a model without (red continuous line) and with copy number effect (blue discontinuous line) was fitted [17]. Similarity between the fitted models indicates that differential PITX2 expression is not copy number-driven.…”

“…To identify genes relevant to HPV-induced carcinogenesis, we performed integrative longitudinal differential gene expression analysis, which also takes the genomic copy number into account [17]. In total, expression of 3642 mRNA genes and 106 mature miRNAs (corresponding to 118 miRNA genes) was found to either increase or decrease consistently over time in at least three out of four cell lines analyzed (Tables S1 and S2).…”

“…To identify m(i)RNA genes with temporal differential expression and to study the association between gene expression and underlying copy number changes, the methodology presented in Miok et al was employed [17]. Temporal gene expression analysis was performed with a common spline for the cell lines, to identify consistently altered genes over time at a 5% false-discovery rate (FDR).…”

Ridge estimation of network models from time‐course omics data

“…Based on the chromosomal location DNA copy number transcripts are matched to the mRNA expression features (employing the overlapPlus matching procedure of the sigaR package, van Wieringen et al., ). The miRNA transcripts are a selection on the basis of temporal differential expression (determined using the method of Miok et al., ), that are either up‐ or downregulated in at least three out of four cell lines. Both mRNA gene expression and DNA copy number data then comprise genes, while the miRNA gene expression data includes genes, all measured at time points in cell lines.…”