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

Handle URI:
http://hdl.handle.net/10754/623101
Title:
Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells
Authors:
Triantafillou, Sofia ( 0000-0002-2535-0432 ) ; Lagani, Vincenzo; Heinze-Deml, Christina; Schmidt, Angelika ( 0000-0002-1185-3012 ) ; Tegner, Jesper ( 0000-0002-9568-5588 ) ; Tsamardinos, Ioannis
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 Casual Discovery on Mass Cytometry Data of Human Immune Cells. Available: http://dx.doi.org/10.1101/122572.
Publisher:
Cold Spring Harbor Laboratory Press
Issue Date:
31-Mar-2017
DOI:
10.1101/122572
Type:
Working Paper
Sponsors:
The authors would like to thank Karen Sachs for offering valuable insights on mass cytometry, and Joris Mooij for helpful discussions. ST, VL and IT 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:
http://biorxiv.org/content/early/2017/03/30/122572
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.accessioned2017-04-10T07:49:51Z-
dc.date.available2017-04-10T07:49:51Z-
dc.date.issued2017-03-31en
dc.identifier.citationTriantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, et al. (2017) Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells. Available: http://dx.doi.org/10.1101/122572.en
dc.identifier.doi10.1101/122572en
dc.identifier.urihttp://hdl.handle.net/10754/623101-
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. ST, VL and IT 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.publisherCold Spring Harbor Laboratory Pressen
dc.relation.urlhttp://biorxiv.org/content/early/2017/03/30/122572en
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titlePredicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cellsen
dc.typeWorking Paperen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionDepartment of Computer Science, University of Crete, Greeceen
dc.contributor.institutionDepartment of Physical Medicine and Rehabilitation, Northwestern University, Chicago, ILen
dc.contributor.institutionSeminar for Statistics, ETH Zurich, Switzerlanden
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet & Karolinska University Hospital, Science for Life Laboratory, Stockholm, Swedenen
kaust.authorTegner, Jesperen
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