A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection
We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2014-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S13784 |
| _version_ | 1818518913937309696 |
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| author | Alberto Cassese Michele Guindani Marina Vannucci |
| author_facet | Alberto Cassese Michele Guindani Marina Vannucci |
| author_sort | Alberto Cassese |
| collection | DOAJ |
| description | We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation. |
| first_indexed | 2024-12-11T01:16:48Z |
| format | Article |
| id | doaj.art-68bed24228f4471bb95346125571dacc |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-11T01:16:48Z |
| publishDate | 2014-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-68bed24228f4471bb95346125571dacc2022-12-22T01:25:51ZengSAGE PublishingCancer Informatics1176-93512014-01-0113s210.4137/CIN.S13784A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable SelectionAlberto Cassese0Michele Guindani1Marina Vannucci2Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Statistics, Rice University, Houston, TX, USA.We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation.https://doi.org/10.4137/CIN.S13784 |
| spellingShingle | Alberto Cassese Michele Guindani Marina Vannucci A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection Cancer Informatics |
| title | A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection |
| title_full | A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection |
| title_fullStr | A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection |
| title_full_unstemmed | A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection |
| title_short | A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection |
| title_sort | bayesian integrative model for genetical genomics with spatially informed variable selection |
| url | https://doi.org/10.4137/CIN.S13784 |
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