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Updated: Jan 7, 2026

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Predicting the unseen: A diffusion-based debiasing framework for transcriptional response prediction at single-cell

Ergan Shang1, Yuting Wei2, Kathryn Roeder1,3

  • 1Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America
|December 26, 2025
PubMed
Summary

dbDiffusion accurately predicts cellular responses to genetic perturbations using a novel generative framework. This computational approach enhances the utility of single-cell CRISPR perturbation experiments by inferring unmeasured gene expression profiles.

Keywords:
CRISPRPerturb-seqdifferential gene expressiondiffusionprediction-powered inference

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Predicting cellular responses to genetic perturbations is crucial for understanding gene regulation.
  • Single-cell CRISPR perturbation assays like Perturb-seq offer direct gene function measurements but are limited by scale.
  • Computational methods are needed to infer responses to unmeasured perturbations from existing data.

Purpose of the Study:

  • To introduce dbDiffusion, a generative framework for predicting transcriptomic responses to unmeasured genetic perturbations.
  • To leverage diffusion models and variational autoencoders for accurate inference of gene expression profiles.
  • To provide a scalable computational framework that extends the utility of Perturb-seq experiments.

Main Methods:

  • Developed dbDiffusion, integrating diffusion models with classifier-free guidance in a variational autoencoder latent space.
  • Utilized biological similarities in gene expression and perturbation relationships for conditional generation.
  • Employed embeddings from measured perturbations to generalize to unseen perturbations, avoiding reliance on large language models.
  • Integrated prediction-powered inference to correct generative model biases and enable rigorous downstream analysis.

Main Results:

  • dbDiffusion demonstrated superior accuracy in predicting perturbation responses compared to state-of-the-art methods on Perturb-seq datasets.
  • The framework successfully generated gene expression profiles for unobserved perturbations by exploiting biological similarities.
  • Prediction-powered inference enabled statistically rigorous identification of differentially expressed genes.

Conclusions:

  • dbDiffusion offers a scalable and accurate computational framework for predicting and analyzing transcriptomic perturbation responses.
  • The method effectively transfers information across related experimental conditions, enhancing the predictive power of perturbation data.
  • This approach significantly extends the capabilities and applications of single-cell CRISPR perturbation assays.