SCENERY: a web application for (causal) network reconstruction from cytometry data

Handle URI:
http://hdl.handle.net/10754/623682
Title:
SCENERY: a web application for (causal) network reconstruction from cytometry data
Authors:
Papoutsoglou, Georgios; Athineou, Giorgos; Lagani, Vincenzo; Xanthopoulos, Iordanis; Schmidt, Angelika; Éliás, Szabolcs; Tegner, Jesper ( 0000-0002-9568-5588 ) ; Tsamardinos, Ioannis
Abstract:
Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Papoutsoglou G, Athineou G, Lagani V, Xanthopoulos I, Schmidt A, et al. (2017) SCENERY: a web application for (causal) network reconstruction from cytometry data. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gkx448.
Publisher:
Oxford University Press (OUP)
Journal:
Nucleic Acids Research
Issue Date:
8-May-2017
DOI:
10.1093/nar/gkx448
Type:
Article
ISSN:
0305-1048; 1362-4962
Sponsors:
The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 617393; CAUSALPATH - Next Generation Causal Analysis project. Funding for open access charge: ERC.
Additional Links:
https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx448
Appears in Collections:
Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorPapoutsoglou, Georgiosen
dc.contributor.authorAthineou, Giorgosen
dc.contributor.authorLagani, Vincenzoen
dc.contributor.authorXanthopoulos, Iordanisen
dc.contributor.authorSchmidt, Angelikaen
dc.contributor.authorÉliás, Szabolcsen
dc.contributor.authorTegner, Jesperen
dc.contributor.authorTsamardinos, Ioannisen
dc.date.accessioned2017-05-22T06:58:04Z-
dc.date.available2017-05-22T06:58:04Z-
dc.date.issued2017-05-08en
dc.identifier.citationPapoutsoglou G, Athineou G, Lagani V, Xanthopoulos I, Schmidt A, et al. (2017) SCENERY: a web application for (causal) network reconstruction from cytometry data. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gkx448.en
dc.identifier.issn0305-1048en
dc.identifier.issn1362-4962en
dc.identifier.doi10.1093/nar/gkx448en
dc.identifier.urihttp://hdl.handle.net/10754/623682-
dc.description.abstractFlow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/.en
dc.description.sponsorshipThe research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 617393; CAUSALPATH - Next Generation Causal Analysis project. Funding for open access charge: ERC.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx448en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleSCENERY: a web application for (causal) network reconstruction from cytometry dataen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNucleic Acids Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionComputer Science Department, University of Crete, Heraklion, Crete 700 13, Greece.en
dc.contributor.institutionGnosis Data Analysis I.K.E., Heraklion, Crete 71305, Greece.en
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm 171 76, Sweden.en
kaust.authorTegner, Jesperen
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