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dc.contributor.authorCassese, Alberto
Guindani, Michele
Antczak, Philipp
Falciani, Francesco
Vannucci, Marina
dc.date.accessioned 2017-05-12T15:04:32Z
dc.date.available 2017-05-12T15:04:32Z
dc.date.issued 2015
dc.identifier.citation Cassese, Alberto, Guindani, Michele, Antczak, Philipp, et al.. "A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants." Biometrics, 71, no. 3 (2015) Wiley: 803-811. http://dx.doi.org/10.1111/biom.12303.
dc.identifier.urihttps://hdl.handle.net/1911/94231
dc.description.abstract In this article we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as "ponds," of progressively more contaminated water. We model the expected expression of a gene in a pond as the sum of the mean of the same gene in the previous pond plus a gene-pond specific difference. We incorporate a variable selection mechanism for the identification of the differential expressions, with a prior distribution on the probability of a change that accounts for the available information on the concentration of chemical compounds present in the water. We carry out posterior inference via MCMC stochastic search techniques. In the application, we reduce the complexity of the data by grouping genes according to their functional characteristics, based on the KEGG pathway database. This also increases the biological interpretability of the results. Our model successfully identifies a number of pathways that show differential expression between consecutive purification stages. We also find that changes in the transcriptional response are more strongly associated to the presence of certain compounds, with the remaining contributing to a lesser extent. We discuss the sensitivity of these results to the model parameters that measure the influence of the prior information on the posterior inference.
dc.language.iso eng
dc.publisher Wiley
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Wiley.
dc.title A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants
dc.type Journal article
dc.citation.journalTitle Biometrics
dc.subject.keywordBayesian inference
Daphnia magna
environmental toxicology
Probit prior
Transcriptomics
variable selection
dc.citation.volumeNumber 71
dc.citation.issueNumber 3
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1111/biom.12303
dc.identifier.pmcid PMC4880373
dc.identifier.pmid 25771699
dc.type.publication post-print
dc.citation.firstpage 803
dc.citation.lastpage 811


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