A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants

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dc.citation.firstpage803en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleBiometricsen_US
dc.citation.lastpage811en_US
dc.citation.volumeNumber71en_US
dc.contributor.authorCassese, Albertoen_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorAntczak, Philippen_US
dc.contributor.authorFalciani, Francescoen_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2017-05-12T15:04:32Zen_US
dc.date.available2017-05-12T15:04:32Zen_US
dc.date.issued2015en_US
dc.description.abstractIn 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.en_US
dc.identifier.citationCassese, Alberto, Guindani, Michele, Antczak, Philipp, et al.. "A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants." <i>Biometrics,</i> 71, no. 3 (2015) Wiley: 803-811. http://dx.doi.org/10.1111/biom.12303.en_US
dc.identifier.doihttp://dx.doi.org/10.1111/biom.12303en_US
dc.identifier.urihttps://hdl.handle.net/1911/94231en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Wiley.en_US
dc.subject.keywordBayesian inferenceen_US
dc.subject.keywordDaphnia magnaen_US
dc.subject.keywordenvironmental toxicologyen_US
dc.subject.keywordProbit prioren_US
dc.subject.keywordTranscriptomicsen_US
dc.subject.keywordvariable selectionen_US
dc.titleA Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutantsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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