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What Do Single-cell Models Already Know About Perturbations?

Home
What Do Single-cell Models Already Know About Perturbations?

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What Do Single-Cell Models Already Know About Perturbations?

Andreas Bjerregaard^1,2, Iñigo Prada-Luengo^2,3, Vivek Das⁴

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

|December 30, 2025

View abstract on PubMed

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 AI explainable AI gene expression generative models in silico perturbations machine learning single-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.