Learning genetic perturbation effects with variational causal inference | JoVE Visualize

Area of Science:

Genomics
Computational Biology
Systems Biology

Background:

Perturb-seq enables high-resolution single-cell transcriptomic profiling of genetic perturbations.
Current computational models struggle with generalizing to unseen perturbations due to overfitting or overly simplistic assumptions.
Accurate prediction of gene regulatory network (GRN) responses is crucial for functional genomics and identifying therapeutic targets.

Purpose of the Study:

To develop a novel computational model that accurately predicts transcriptomic responses to genetic perturbations, particularly for unseen perturbations.
To integrate mechanistic causal inference with deep learning for enhanced extrapolation capabilities in single-cell data analysis.
To provide a robust tool for interpreting and simulating gene perturbation effects at the single-cell level.

Main Methods:

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

Main Results:

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

Conclusions:

SCCVAE offers a robust and accurate method for predicting gene regulatory network dynamics following perturbations.
The hybrid approach overcomes limitations of purely deep learning or mechanistic models, enhancing predictive power for novel perturbations.
This tool advances the interpretation of single-cell transcriptomic data and aids in the discovery of therapeutic strategies.