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dc.contributor.authorJimenez-Jimenez, Victor 
dc.contributor.authorMarti-Gomez, Carlos 
dc.contributor.authordel Pozo, Miguel Angel 
dc.contributor.authorLara-Pezzi, Enrique 
dc.contributor.authorSanchez-Cabo, Fatima
dc.identifier.citationBioinformatics. 2021:65-87es_ES
dc.description.abstractOmics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers.es_ES
dc.description.sponsorshipFS-C received support from the Spanish Ministerio de Economía y Competitividad [grant no. RTI2018-102084-B-I00]; EL-P received support from the Spanish Ministerio de Economía y Competitividad (RTI2018-096961-B-I00), from the European Union (CardioNeT-ITN-289600 and CardioNext-ITN-608027) and the Spanish Carlos III Institute of Health (RD12/0042/066); M.A.d.P received support from the Spanish Ministerio de Economía y Competitividad (SAF2017-83130-R) and from the European Union Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement nº 641639 BIOPOL-ITN-641639; V.J-J. received an ESR contract from BIOPOL-ITN-641639). M.A.d.P is member of the Tec4Bio consortium (ref. S2018/NMT4443). The CNIC is supported by MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence [MCIU award SEV-2015-0505].es_ES
dc.publisherExon Publications es_ES
dc.titleBayesian Inference of Gene Expressiones_ES
dc.typebook partes_ES
dc.rights.licenseAtribución-NoComercial 4.0 Internacional*
dc.contributor.funderMinisterio de Economía y Competitividad (España) 
dc.contributor.funderInstituto de Salud Carlos III 
dc.contributor.funderUnión Europea. Comisión Europea. H2020 
dc.contributor.funderFundación ProCNIC 
dc.contributor.funderMinisterio de Ciencia e Innovación (España) 
dc.repisalud.orgCNICCNIC::Grupos de investigación::Señalización por Integrinases_ES
dc.repisalud.orgCNICCNIC::Grupos de investigación::Regulación Molecular de la Insuficiencia Cardiacaes_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Bioinformáticaes_ES
dc.rights.accessRightsopen accesses_ES

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