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

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 experi...

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Autores principales:	Sofia Triantafillou, Vincenzo Lagani, Christina Heinze-Deml, Angelika Schmidt, Jesper Tegner, Ioannis Tsamardinos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ec82a6e1b2fd40ee8b1e4ad2d4552b47
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spelling	oai:doaj.org-article:ec82a6e1b2fd40ee8b1e4ad2d4552b472021-12-02T15:06:14ZPredicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells10.1038/s41598-017-08582-x2045-2322https://doaj.org/article/ec82a6e1b2fd40ee8b1e4ad2d4552b472017-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08582-xhttps://doaj.org/toc/2045-2322Abstract 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.Sofia TriantafillouVincenzo LaganiChristina Heinze-DemlAngelika SchmidtJesper TegnerIoannis TsamardinosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sofia Triantafillou Vincenzo Lagani Christina Heinze-Deml Angelika Schmidt Jesper Tegner Ioannis Tsamardinos Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
description	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.
format	article
author	Sofia Triantafillou Vincenzo Lagani Christina Heinze-Deml Angelika Schmidt Jesper Tegner Ioannis Tsamardinos
author_facet	Sofia Triantafillou Vincenzo Lagani Christina Heinze-Deml Angelika Schmidt Jesper Tegner Ioannis Tsamardinos
author_sort	Sofia Triantafillou
title	Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
title_short	Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
title_full	Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
title_fullStr	Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
title_full_unstemmed	Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
title_sort	predicting causal relationships from biological data: applying automated causal discovery on mass cytometry data of human immune cells
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/ec82a6e1b2fd40ee8b1e4ad2d4552b47
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Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

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