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Related Experiment Video

Updated: Jun 13, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Learning Genetic Perturbation Effects with Variational Causal Inference.

Emily Liu^1,2, Jiaqi Zhang^1,2,3, Caroline Uhler^1,2,3

¹Department of Electrical Engineering and Computer Science, MIT.

Biorxiv : the Preprint Server for Biology

|June 12, 2025

Summary

This summary is machine-generated.

We developed a hybrid computational model, Single Cell Causal Variational Autoencoder (SCCVAE), to predict cellular responses to genetic perturbations. SCCVAE outperforms existing methods in forecasting unseen genetic changes, aiding functional genomics and therapeutic target identification.

Keywords:

Causal Inference Perturb-seq Perturbation Modeling Variational Inference

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Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Advances in sequencing, like Perturb-seq, enable single-cell transcriptomic profiling of genetic perturbations.
Existing computational models struggle with generalization: deep learning overfits, while mechanistic models are too simplistic for large-scale data.

Purpose of the Study:

To develop a hybrid computational model integrating mechanistic causal inference and deep learning for robust prediction of cellular responses to genetic perturbations.
To improve extrapolation capabilities for unseen perturbations and enhance interpretability of single-cell transcriptomic data.

Main Methods:

Proposed the Single Cell Causal Variational Autoencoder (SCCVAE), a hybrid model combining a learned gene regulatory network with a variational autoencoder.
The mechanistic component models perturbations as shift interventions propagating through the regulatory network.
Integrated this into a deep learning framework to generate comprehensive transcriptomic responses.

Main Results:

SCCVAE demonstrated superior performance in predicting responses to unseen genetic perturbations compared to state-of-the-art methods.
The model's latent space facilitated identification of functional perturbation modules.
Enabled simulation of gene knockdown experiments with varying penetrance.

Conclusions:

SCCVAE offers a robust tool for interpreting and interpolating single-cell perturbational responses.
The hybrid approach effectively balances mechanistic understanding with deep learning's capacity for complex data.
This method advances functional genomics and aids in identifying potential therapeutic targets.