Applying causal discovery to single-cell analyses using CausalCell

Abstract
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Abstract

Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Inferred causal interactions in single cells provide valuable clues for investigating molecular interaction and gene regulation, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions. The report of causal discovery methods and generation of single-cell data make applying causal discovery to single-cells a promising direction. However, how to evaluate and choose causal discovery methods and how to develop workflow and platform remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analysing multiple scRNA-seq datasets. Our results suggest that different situations call for different methods and the constraint-based PC algorithm plus kernel-based conditional independence tests suit for most situations. Relevant issues are discussed and tips for best practices are recommended.

Data availability

Only public data were used. Links to all data are provided in the manuscript.

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Article and author information

Author details

Yujian Wen

Bioinformatics Section, Southern Medical University, Guangzhou, China

Competing interests
The authors declare that no competing interests exist.
Jielong Huang

Bioinformatics Section, Southern Medical University, Guangzhou, China

Competing interests
The authors declare that no competing interests exist.
Shuhui Guo

Bioinformatics Section, Southern Medical University, Guangzhou, China

Competing interests
The authors declare that no competing interests exist.
Yehezqel Elyahu

The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Competing interests
The authors declare that no competing interests exist.
Alon Monsonego

The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel

Competing interests
The authors declare that no competing interests exist.
Hai Zhang

Network Center, Southern Medical University, Guangzhou, China

For correspondence
zhangh@smu.edu.cn

Competing interests
The authors declare that no competing interests exist.
Yanqing Ding

Department of Pathology, Southern Medical University, Guangzhou, China

For correspondence
dyqgz@126.com

Competing interests
The authors declare that no competing interests exist.
Hao Zhu

Bioinformatics Section, Southern Medical University, Guangzhou, China

For correspondence
zhuhao@smu.edu.cn

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7384-3840

Funding

National Natural Science Foundation of China (31771456)

Hao Zhu

Department of Science and Technology of Guangdong Province (2020A1515010803)

Hao Zhu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.