Biclustering via Semiparametric Bayesian Inference

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dc.contributor.authorMurua, Alejandro
dc.contributor.authorQuintana, Fernando Andres
dc.date.accessioned2025-01-20T21:00:56Z
dc.date.available2025-01-20T21:00:56Z
dc.date.issued2022
dc.description.abstractMotivated by classes of problems frequently found in the analysis of gene expression data, we propose a semiparametric Bayesian model to detect biclusters, that is, subsets of individuals sharing similar patterns over a set of conditions. Our approach is based on the well-known plaid model by Lazzeroni and Owen (2002). By assuming a truncated stick-breaking prior we also find the number of biclusters present in the data as part of the inference. Evidence from a simulation study shows that the model is capable of correctly detecting biclusters and performs well compared to some competing approaches. The flexibility of the proposed prior is demonstrated with applications to the analysis of gene expression data (continuous responses) and histone modifications data (count responses).
dc.fuente.origenWOS
dc.identifier.doi10.1214/21-BA1284
dc.identifier.eissn1936-0975
dc.identifier.issn1931-6690
dc.identifier.urihttps://doi.org/10.1214/21-BA1284
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92780
dc.identifier.wosidWOS:000911408000002
dc.issue.numero3
dc.language.isoen
dc.pagina.final995
dc.pagina.inicio969
dc.revistaBayesian analysis
dc.rightsacceso restringido
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBiclustering via Semiparametric Bayesian Inference
dc.typeartículo
dc.volumen17
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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