ArticleeLife2023
Applying causal discovery to single-cell analyses using CausalCell.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R.Behavior research methods · 2026Review
- Reference-guided computational framework identifies microenvironment metabolic subtypes and targets using pan-cancer single-cell datasets.Genome medicine · 2025Article
- Unpacking PCOS Inflammation: From Misconceptions to Immune Networks.Endocrinology · 2025Review
- Transcriptome-wide root causal inference.PLoS computational biology · 2025Article
- Discovering governing equations of biological systems through representation learning and sparse model discovery.NAR genomics and bioinformatics · 2025Article
- Article
- Causal machine learning for single-cell genomics.Nature genetics · 2025Review
- Article
- Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes.Research (Washington, D.C.) · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Discovering Root Causal Genes with High Throughput Perturbations.bioRxiv : the preprint server for biology · 2024Article
- Transcriptome-Wide Root Causal Inference.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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. Reported causal discovery methods and single-cell datasets make applying causal discovery to single cells a promising direction. However, evaluating and choosing causal discovery methods and developing and performing proper workflow 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 analyzing multiple single-cell RNA-sequencing (scRNA-seq) datasets. Our results suggest that different situations need different methods and the constraint-based PC algorithm with kernel-based conditional independence tests work best in most situations. Related issues are discussed and tips for best practices are given. Inferred causal interactions in single cells provide valuable clues for investigating molecular interactions and gene regulations, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.