Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD

Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a...

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Autores: Marcon, Yannick|||0000-0003-0138-2023, Bishop, Tom|||0000-0002-3407-2526, Avraam, Demetris|||0000-0001-8908-2441, Escribà-Montagut, Xavier, Ryser-Welch, Patricia, Wheater, Stuart, Burton, Paul|||0000-0001-5799-9634, González, Juan Ramón|||0000-0003-3267-2146
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:255388
Acceso en línea:https://ddd.uab.cat/record/255388
https://dx.doi.org/urn:doi:10.1371/journal.pcbi.1008880
Access Level:acceso abierto
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dc.title.none.fl_str_mv Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
title Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
spellingShingle Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
Marcon, Yannick|||0000-0003-0138-2023
title_short Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
title_full Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
title_fullStr Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
title_full_unstemmed Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
title_sort Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD
dc.creator.none.fl_str_mv Marcon, Yannick|||0000-0003-0138-2023
Bishop, Tom|||0000-0002-3407-2526
Avraam, Demetris|||0000-0001-8908-2441
Escribà-Montagut, Xavier
Ryser-Welch, Patricia
Wheater, Stuart
Burton, Paul|||0000-0001-5799-9634
González, Juan Ramón|||0000-0003-3267-2146
author Marcon, Yannick|||0000-0003-0138-2023
author_facet Marcon, Yannick|||0000-0003-0138-2023
Bishop, Tom|||0000-0002-3407-2526
Avraam, Demetris|||0000-0001-8908-2441
Escribà-Montagut, Xavier
Ryser-Welch, Patricia
Wheater, Stuart
Burton, Paul|||0000-0001-5799-9634
González, Juan Ramón|||0000-0003-3267-2146
author_role author
author2 Bishop, Tom|||0000-0002-3407-2526
Avraam, Demetris|||0000-0001-8908-2441
Escribà-Montagut, Xavier
Ryser-Welch, Patricia
Wheater, Stuart
Burton, Paul|||0000-0001-5799-9634
González, Juan Ramón|||0000-0003-3267-2146
author2_role author
author
author
author
author
author
author
description Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture (" resources ") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (). Data sharing enhances understanding of research results beyond what is possible from any single study. Data pooling across multiple studies increases statistical power and allows exploration of between-study heterogeneity. But, considerations related to ethico-legal and intellectual/commercial value regularly prevent or impede physical data sharing. DataSHIELD is designed to circumvent this problem. However, despite the growing confidence users have been placing in DataSHIELD to perform privacy-protected analyses of data in cohort consortia, there are real challenges to federated analytics. They include considering the wide range of data formats, and big data sources used, for example, in 'omics-based research. This article describes the development and implementation of the new "resources" architecture in DataSHIELD that overcomes this limitation. We illustrate its value with real world examples related to genomics and geographical data. We also demonstrate how genomic data sharing initiatives such as GA4GH and EGA can benefit directly from our development. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects.
publishDate 2021
dc.date.none.fl_str_mv 2
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
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format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/255388
https://dx.doi.org/urn:doi:10.1371/journal.pcbi.1008880
url https://ddd.uab.cat/record/255388
https://dx.doi.org/urn:doi:10.1371/journal.pcbi.1008880
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 874583
European Commission https://doi.org/10.13039/501100000780 824989
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-100789-B-I00
Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 CEX2018-000806-S
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
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https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
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spelling Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELDMarcon, Yannick|||0000-0003-0138-2023Bishop, Tom|||0000-0002-3407-2526Avraam, Demetris|||0000-0001-8908-2441Escribà-Montagut, XavierRyser-Welch, PatriciaWheater, StuartBurton, Paul|||0000-0001-5799-9634González, Juan Ramón|||0000-0003-3267-2146Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture (" resources ") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (). Data sharing enhances understanding of research results beyond what is possible from any single study. Data pooling across multiple studies increases statistical power and allows exploration of between-study heterogeneity. But, considerations related to ethico-legal and intellectual/commercial value regularly prevent or impede physical data sharing. DataSHIELD is designed to circumvent this problem. However, despite the growing confidence users have been placing in DataSHIELD to perform privacy-protected analyses of data in cohort consortia, there are real challenges to federated analytics. They include considering the wide range of data formats, and big data sources used, for example, in 'omics-based research. This article describes the development and implementation of the new "resources" architecture in DataSHIELD that overcomes this limitation. We illustrate its value with real world examples related to genomics and geographical data. We also demonstrate how genomic data sharing initiatives such as GA4GH and EGA can benefit directly from our development. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. 22021-01-0120212021-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/255388https://dx.doi.org/urn:doi:10.1371/journal.pcbi.1008880reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 874583European Commission https://doi.org/10.13039/501100000780 824989Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 RTI2018-100789-B-I00Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 CEX2018-000806-Sopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2553882026-06-06T12:50:31Z
score 15,300724

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