Applying causal discovery to single-cell analyses using CausalCell
- Southern Medical University, China
- Ben-Gurion University of the Negev, Israel
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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Single cell RNA-seq analysis of adult and paediatric IDH-wildtype GlioblastomasNCBI Gene Expression Omnibus, GSE131928.
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T cell landscape of non-small cell lung cancer revealed by deep single-cell RNA sequencingNCBI Gene Expression Omnibus, GSE99254.
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Lineage tracking reveals dynamic relationships of T cells in colorectal cancerNCBI Gene Expression Omnibus, GSE108989.
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Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencingNCBI Gene Expression Omnibus, GSE98638.
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Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell DataScience Supplementary Materials, doi: 10.1126/science.1105809.
Article and author information
Author details
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.
Reviewing Editor
- Babak Momeni, Boston College, United States
Version history
- Received: June 28, 2022
- Preprint posted: August 19, 2022 (view preprint)
- Accepted: May 1, 2023
- Accepted Manuscript published: May 2, 2023 (version 1)
- Version of Record published: May 30, 2023 (version 2)
Copyright
© 2023, Wen et al.
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.
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Applying causal discovery to single-cell analyses using CausalCell
eLife 12:e81464.
https://doi.org/10.7554/eLife.81464
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