Methods for causal inference from gene perturbation experiments and validation
Open accessAbstract
Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae . The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.
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Because gene expression data can be noisy and variable, recent work fuses co-expression matrices with existing biomedical networks such as GO annotations and PPI, then feeds the resulting graph into graph convolution layers.
“Because gene expression data can be noisy and variational, recent advances include fusing the co-expression matrices with existing biomedical networks, such as GO annotations and PPI, and feeding the resulting graph into graph convolution layers (Section 2.2) [150, 151, 152].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [150]
This fusion has enabled more interpretable disease classification models, for example by weighting gene interactions using existing biological knowledge.
“Doing so has enabled more interpretable disease classification models (e.g., weighting gene interactions based on existing biological knowledge).”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [150]
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Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy
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Distributional robustness of K-class estimators and the PULSE
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Self-citationGraphical continuous Lyapunov models
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Model-Based Causal Feature Selection for General Response Types
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Self-citationLocal Causal Discovery in Multiple Manipulated Datasets
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eQTLs as causal instruments for the reconstruction of hormone linked gene networks
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Toward causality and improving external validity
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Self-citationLearning DAGs without imposing acyclicityPreprint
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Perturbations and Causality in Gaussian Latent Variable ModelsPreprint
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Causal Graph Identification Under Soft Intervention
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Perturbation graphs, invariant causal prediction and causal relations in psychology
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- Authors
- Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P.
- Affiliations
- ETH Zurich (CH)
- Bern University of Applied Sciences (CH)
- University of Amsterdam (NL)
- Max Planck Institute for Intelligent Systems (DE)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2016-07-05
- Volume
- 113
- Issue
- 27
- Pages
- 7361-7368
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4941490
- OpenAlex
- W2460828264
- Cited by
- 140
- References
- 53
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 13 · 2023: 14 · 2022: 18 · 2021: 28 · 2020: 18 · 2019: 11 · 2018: 6 · 2017: 5 · 2016: 6
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI grant 639.072.410)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- causal inference, gene deletion, Saccharomyces cerevisiae, causal discovery, intervention prediction, identifiability
- MeSH terms
- Algorithms, Flow Cytometry, Models, Genetic, Saccharomyces cerevisiae, Software, Statistics as Topic, Gene Deletion
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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