Clustering with Communication: A Variational Framework for Single Cell Representation Learning
Cong Qi, Yeqing Chen, Jie Zhang, Wei Zhi·May 08, 2025
Summary
The CCCVAE framework enhances single-cell representation learning, integrating cell communication signals. It surpasses conventional VAEs, offering deeper biological insights through transcriptional similarity and signaling context. Utilizing a variational Gaussian Process, it approximates the intractable posterior in unsupervised single-cell analysis. The text also discusses formulas for sparse Gaussian processes, addressing noise correction and specifying a KL divergence formula. Optimization targets minimizing the ELBO, ensuring accurate accounting of these elements.
Introduction
Background
Overview of single-cell representation learning
Challenges in conventional Variational Autoencoders (VAEs)
Objective
To introduce CCCVAE, a framework that integrates cell communication signals for deeper biological insights
Method
Cell Communication Signal Integration
Explanation of how CCCVAE incorporates cell communication signals
Variational Gaussian Process
Description of the variational Gaussian Process used for approximating the intractable posterior
Unsupervised Single-Cell Analysis
How CCCVAE performs unsupervised analysis on single-cell data
Formulas for Sparse Gaussian Processes
Detailed explanation of formulas used for noise correction
KL Divergence Formula
Presentation of the KL divergence formula in CCCVAE context
Optimization
ELBO Minimization
Explanation of the objective of minimizing the Evidence Lower Bound (ELBO)
Accurate Accounting
Discussion on ensuring accurate accounting of elements in the optimization process
Applications and Benefits
Enhanced Biological Insights
How CCCVAE provides deeper biological insights through transcriptional similarity and signaling context
Case Studies
Examples of applications and benefits in real-world scenarios
Conclusion
Summary of Contributions
Recap of CCCVAE's advancements in single-cell representation learning
Future Directions
Potential areas for further research and development
Basic info
papers
machine learning
artificial intelligence
Advanced features