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dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierNutrition et Neurobiologie intégrée [NutriNeuro]
dc.contributor.authorBRIERE, Marie-Galadriel
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierActions for OnCogenesis understanding and Target Identification in ONcology [ACTION]
dc.contributor.authorDARBO, Elodie
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorTHEBAULT, Patricia
IDREF: 166416738
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorURICARU, Raluca
IDREF: 151193681
dc.date.accessioned2021-10-19T13:58:35Z
dc.date.available2021-10-19T13:58:35Z
dc.date.issued2021
dc.identifier.issn1471-2105en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/112809
dc.description.abstractEnBackground: Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. Conclusion: We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enConsensus clustering
dc.subject.enData integration
dc.subject.enDisease subtyping
dc.subject.enMulti-omic data
dc.title.enConsensus clustering applied to multi-omics disease subtyping
dc.typeArticle de revueen_US
dc.identifier.doi10.1186/s12859-021-04279-1en_US
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]en_US
dc.identifier.pubmed34229612en_US
bordeaux.journalBMC Bioinformaticsen_US
bordeaux.volume22en_US
bordeaux.hal.laboratoriesNutriNeurO (Laboratoire de Nutrition et Neurobiologie Intégrée) - UMR 1286en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINRAEen_US
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDCentre National de la Recherche Scientifiqueen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=BMC%20Bioinformatics&rft.date=2021&rft.volume=22&rft.issue=1&rft.eissn=1471-2105&rft.issn=1471-2105&rft.au=BRIERE,%20Marie-Galadriel&DARBO,%20Elodie&THEBAULT,%20Patricia&URICARU,%20Raluca&rft.genre=article


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