A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

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dc.contributor.authorOgunnaike, Babatunde A.
dc.contributor.authorGelmi, Claudio A.
dc.contributor.authorEdwards, Jeremy S.
dc.date.accessioned2024-01-10T13:11:12Z
dc.date.available2024-01-10T13:11:12Z
dc.date.issued2010
dc.description.abstractGene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. (C) 2010 Elsevier Ltd. All rights reserved.
dc.description.funderDelaware Biotechnology Institute
dc.description.funderUS Department of Energy
dc.description.funderGenomatica
dc.fechaingreso.objetodigital01-04-2024
dc.format.extent12 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jtbi.2010.02.021
dc.identifier.eissn1095-8541
dc.identifier.issn0022-5193
dc.identifier.pubmedidMEDLINE:20170665
dc.identifier.urihttps://doi.org/10.1016/j.jtbi.2010.02.021
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78014
dc.identifier.wosidWOS:000277055500006
dc.information.autorucIngeniería;Gelmi C;S/I;7637
dc.issue.numero2
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final222
dc.pagina.inicio211
dc.publisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.revistaJOURNAL OF THEORETICAL BIOLOGY
dc.rightsacceso restringido
dc.subjectMixture models
dc.subjectPoisson distributions
dc.subjectGamma distributions
dc.subjectBeta distributions
dc.subjectGene expression
dc.subjectDIFFERENTIALLY EXPRESSED GENES
dc.subjectIONIZING-RADIATION
dc.subjectMIXTURE-MODELS
dc.subjectISSUES
dc.subjectNOISE
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference
dc.typeartículo
dc.volumen264
sipa.codpersvinculados7637
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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