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What Do Single-Cell Models Already Know About Perturbations?

Andreas Bjerregaard1,2, Iñigo Prada-Luengo2,3, Vivek Das4

  • 1Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark.

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|December 30, 2025
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Summary
This summary is machine-generated.

Single-cell generative models implicitly contain perturbation information. Automatic differentiation of decoder outputs reveals gene expression changes, enabling in-silico simulations and pathway analysis.

Keywords:
agentic AIexplainable AIgene expressiongenerative modelsin silico perturbationsmachine learningsingle-cell RNA sequencing

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Area of Science:

  • Computational Biology
  • Single-cell Genomics
  • Machine Learning

Background:

  • Single-cell generative models commonly incorporate virtual cells.
  • The models' capacity to represent biological perturbations is not well understood.
  • Investigating implicit knowledge of perturbations in these models is crucial.

Purpose of the Study:

  • To explore the implicit knowledge of perturbations within single-cell generative models.
  • To develop a method for inferring and visualizing gene expression changes due to perturbations.
  • To evaluate the utility of these models for in-silico perturbation simulations and pathway analysis.

Main Methods:

  • Trained variational autoencoders on diverse gene expression datasets (genetic, chemical, temporal perturbations).
  • Inferred perturbations by computing vector fields of infinitesimal gene expression changes using automatic differentiation.
  • Probed a large-scale scVI model, scoring genes by gradient alignment with healthy-to-disease axes and using LLMs for pathway evaluation.

Main Results:

  • Successfully recovered known biological transitions in various perturbation datasets (e.g., gene knockout, chemical treatment, embryogenesis).
  • Identified pathways with significant relevance to type 2 diabetes in a large mouse cell atlas dataset.
  • Demonstrated that gradient-based methods outperform average expression baselines for perturbation analysis.

Conclusions:

  • Trained single-cell decoders inherently store rich, perturbation-relevant information.
  • Automatic differentiation provides a powerful tool to access this information for in-silico simulations.
  • This approach enables principled ranking of genes and pathways along disease or treatment axes without requiring explicit perturbation labels or specialized model architectures.