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.
Correlation between objects does not answer many scientific questions because of the lack of causal but the excess of spurious information and is prone to happen by coincidence. Causal discovery infers causal relationships from data upon conditional independence test between objects without prior assumptions (e.g., variables have linear relationships and data follow the Gaussian distribution). Causal interactions within and between cells provide valuable information for investigating gene regulation, identifying diagnostic and therapeutic targets, and designing experimental and clinical studies. The rapid increase of single-cell data permits inferring causal interactions in many cell types. However, because no algorithms have been designed for handling abundant variables and few algorithms have been evaluated using real data, how to apply causal discovery to single-cell data remains a challenge. We report a pipeline and web server (http://www.gaemons.net/causalcell/causalDiscovery/) for accurately and conveniently performing causal discovery. The pipeline has been developed upon the benchmarking of 18 algorithms and the analyses of multiple datasets. Our applications indicate that only complicated algorithms can generate satisfactorily reliable results. Critical issues are discussed, and tips for best practices are provided.
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