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CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition

Zhang, Chengming, Cai, Deyu, Aihara, Kazuyuki, Chen, Luonan·2026·Nature Methods
DOI 10.1038/s41592-026-03274-9
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Abstract

Biological systems exhibit complex hierarchical regulation, with interactions across multiple layers governing transitions between states. Identifying the causal architecture underlying these state transitions remains challenging because confounding can produce spurious associations in observational data. Here we present CauTrigger, a hierarchical deep latent variable model that integrates do-calculus to identify potential causal triggers and predict perturbation outcomes across multiple regulatory layers. By disentangling causal and spurious latent factors, CauTrigger recursively reconstructs the underlying regulatory hierarchy and enables interpretable reasoning from observational data. Across synthetic and real-world datasets with multilayer regulatory structures, CauTrigger outperforms existing methods in identifying causal triggers and forecasting system-level responses. Applied to single-cell multi-omics and gut microbiome datasets, CauTrigger prioritizes candidate causal triggers and uses in silico perturbations to generate testable hypotheses about biological state transitions and inform potential strategies for state navigation. Together, these results establish CauTrigger as a general-purpose framework for causal discovery in complex biological systems.

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  1. Pearl, J. Causal inference. In Proc. Workshop on Causality: Objectives and Assessment at NIPS 2008 Vol. 6 (eds Guyon, I. et al.) 39–58 (PMLR, 2010).
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Authors
Zhang, C., Cai, D., Aihara, K., & Chen, L.
Affiliations
  • Shanghai Jiao Tong University (CN)
  • The University of Tokyo Institutes for Advanced Study (JP)
Corresponding author
  • Aihara, Kazuyuki
  • Chen, Luonan
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2026-10-22
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-026-03274-9
OpenAlex
W7223169918
Cited by
0
References
90
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nature Methods (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords
causal inference, gene regulatory networks, single-cell trajectory analysis

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