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dc.contributor.authorPlanell, Nuria
dc.contributor.authorLagani, Vincenzo
dc.contributor.authorSebastian-Leon, Patricia
dc.contributor.authorvan der Kloet, Frans
dc.contributor.authorEwing, Ewoud
dc.contributor.authorKarathanasis, Nestoras
dc.contributor.authorUrdangarin, Arantxa
dc.contributor.authorArozarena, Imanol
dc.contributor.authorJagodic, Maja
dc.contributor.authorTsamardinos, Ioannis
dc.contributor.authorTarazona, Sonia
dc.contributor.authorConesa, Ana
dc.contributor.authorTegner, Jesper
dc.contributor.authorGomez-Cabrero, David
dc.date.accessioned2021-03-24T05:55:34Z
dc.date.available2020-12-09T13:21:04Z
dc.date.available2021-03-24T05:55:34Z
dc.date.issued2021-03-22
dc.date.submitted2020-10-22
dc.identifier.citationPlanell, N., Lagani, V., Sebastian-Leon, P., van der Kloet, F., Ewing, E., Karathanasis, N., … Gomez-Cabrero, D. (2021). STATegra: Multi-Omics Data Integration – A Conceptual Scheme With a Bioinformatics Pipeline. Frontiers in Genetics, 12. doi:10.3389/fgene.2021.620453
dc.identifier.issn1664-8021
dc.identifier.pmid33747045
dc.identifier.doi10.3389/fgene.2021.620453
dc.identifier.urihttp://hdl.handle.net/10754/666319
dc.description.abstractTechnologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.
dc.description.sponsorshipWe thank all members of the STATegra consortium for their contributions to this work.
dc.description.sponsorshipThis work has been funded by the European Union Seventh Framework Programme (FP7/2007–2013) under the grant agreement 306000-STATegra.
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fgene.2021.620453/full
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleSTATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
dc.identifier.journalFrontiers in genetics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUnit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
dc.contributor.institutionScience for Life Laboratory, Solna, Sweden.
dc.contributor.institutionTranslational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain.
dc.contributor.institutionMucosal & Salivary Biology DivisionKing’s College London Dental Institute, London, United Kingdom.
dc.identifier.volume12
kaust.personTegner, Jesper
kaust.personGomez-Cabrero, David
dc.date.accepted2021-01-20
refterms.dateFOA2020-12-09T13:22:36Z


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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted
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