Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

Handle URI:
http://hdl.handle.net/10754/625840
Title:
Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
Authors:
Triantafillou, Sofia ( 0000-0002-2535-0432 ) ; Lagani, Vincenzo ( 0000-0002-6552-6076 ) ; Heinze-Deml, Christina; Schmidt, Angelika ( 0000-0002-1185-3012 ) ; Tegner, Jesper ( 0000-0002-9568-5588 ) ; Tsamardinos, Ioannis ( 0000-0002-2492-959X )
Abstract:
Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Triantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, et al. (2017) Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports 7. Available: http://dx.doi.org/10.1038/s41598-017-08582-x.
Publisher:
Springer Nature
Journal:
Scientific Reports
Issue Date:
29-Sep-2017
DOI:
10.1038/s41598-017-08582-x
Type:
Article
ISSN:
2045-2322
Sponsors:
The authors would like to thank Karen Sachs for offering valuable insights on mass cytometry, and Joris Mooij for helpful discussions. S.T., V.L. and I.T. were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 617393.
Additional Links:
https://www.nature.com/articles/s41598-017-08582-x
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.authorTriantafillou, Sofiaen
dc.contributor.authorLagani, Vincenzoen
dc.contributor.authorHeinze-Deml, Christinaen
dc.contributor.authorSchmidt, Angelikaen
dc.contributor.authorTegner, Jesperen
dc.contributor.authorTsamardinos, Ioannisen
dc.date.accessioned2017-10-09T09:03:13Z-
dc.date.available2017-10-09T09:03:13Z-
dc.date.issued2017-09-29en
dc.identifier.citationTriantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, et al. (2017) Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports 7. Available: http://dx.doi.org/10.1038/s41598-017-08582-x.en
dc.identifier.issn2045-2322en
dc.identifier.doi10.1038/s41598-017-08582-xen
dc.identifier.urihttp://hdl.handle.net/10754/625840-
dc.description.abstractLearning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.en
dc.description.sponsorshipThe authors would like to thank Karen Sachs for offering valuable insights on mass cytometry, and Joris Mooij for helpful discussions. S.T., V.L. and I.T. were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 617393.en
dc.publisherSpringer Natureen
dc.relation.urlhttps://www.nature.com/articles/s41598-017-08582-xen
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titlePredicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cellsen
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.journalScientific Reportsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Computer Science, University of Crete, Rethimno, Greece.en
dc.contributor.institutionDepartment of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.en
dc.contributor.institutionSeminar for Statistics, ETH Zurich, Zurich, Switzerland.en
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet & Karolinska University Hospital, Science for Life Laboratory, Stockholm, Sweden.en
kaust.authorTegner, Jesperen
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