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

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Summary

We developed dbDiffusion, a computational tool that accurately predicts cellular responses to genetic perturbations. This method enhances the utility of Perturb-seq experiments by inferring gene expression changes for unmeasured genetic alterations.

Keywords:
CRISPRDifferential gene expressionDiffusionPerturb-seqPrediction-Powered Inference

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Area of Science:

  • Computational Biology
  • Genomics
  • Gene Regulation

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 novel generative framework for predicting transcriptomic perturbation responses.
  • To enable accurate inference of gene expression changes for unobserved genetic perturbations.
  • To develop a scalable computational approach that extends the utility of Perturb-seq experiments.

Main Methods:

  • dbDiffusion integrates diffusion models with classifier-free guidance within a variational autoencoder (VAE) framework.
  • The model operates in latent space, leveraging biological similarities in gene expression and perturbation relationships.
  • It utilizes embeddings from measured perturbations to generalize to unseen perturbations, avoiding reliance on large language models.

Main Results:

  • dbDiffusion demonstrated superior accuracy in predicting perturbation responses compared to state-of-the-art methods on Perturb-seq datasets.
  • The framework successfully generates gene expressions for previously unobserved perturbations by exploiting biological similarities.
  • Integration of prediction-powered inference corrects generative model biases, enabling statistically rigorous downstream analyses like differential gene expression identification.

Conclusions:

  • dbDiffusion provides a scalable and accurate computational framework for predicting and analyzing transcriptomic perturbation responses.
  • The method enhances the cost-effectiveness and scope of single-cell perturbation experiments.
  • This approach advances the understanding of gene regulation by enabling predictions for a wider range of genetic perturbations.