Causal effects in microbiomes using interventional calculus

Abstract Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Musfiqur Sazal, Vitalii Stebliankin, Kalai Mathee, Changwon Yoo, Giri Narasimhan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0974e667ee9d426d989defda541b8088
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0974e667ee9d426d989defda541b8088
record_format	dspace
spelling	oai:doaj.org-article:0974e667ee9d426d989defda541b80882021-12-02T13:20:01ZCausal effects in microbiomes using interventional calculus10.1038/s41598-021-84905-32045-2322https://doaj.org/article/0974e667ee9d426d989defda541b80882021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84905-3https://doaj.org/toc/2045-2322Abstract Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.Musfiqur SazalVitalii StebliankinKalai MatheeChangwon YooGiri NarasimhanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Musfiqur Sazal Vitalii Stebliankin Kalai Mathee Changwon Yoo Giri Narasimhan Causal effects in microbiomes using interventional calculus
description	Abstract Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.
format	article
author	Musfiqur Sazal Vitalii Stebliankin Kalai Mathee Changwon Yoo Giri Narasimhan
author_facet	Musfiqur Sazal Vitalii Stebliankin Kalai Mathee Changwon Yoo Giri Narasimhan
author_sort	Musfiqur Sazal
title	Causal effects in microbiomes using interventional calculus
title_short	Causal effects in microbiomes using interventional calculus
title_full	Causal effects in microbiomes using interventional calculus
title_fullStr	Causal effects in microbiomes using interventional calculus
title_full_unstemmed	Causal effects in microbiomes using interventional calculus
title_sort	causal effects in microbiomes using interventional calculus
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0974e667ee9d426d989defda541b8088
work_keys_str_mv	AT musfiqursazal causaleffectsinmicrobiomesusinginterventionalcalculus AT vitaliistebliankin causaleffectsinmicrobiomesusinginterventionalcalculus AT kalaimathee causaleffectsinmicrobiomesusinginterventionalcalculus AT changwonyoo causaleffectsinmicrobiomesusinginterventionalcalculus AT girinarasimhan causaleffectsinmicrobiomesusinginterventionalcalculus
_version_	1718393252449615872

Causal effects in microbiomes using interventional calculus

Ejemplares similares