Correlated latent space learning for structural differentiation modeling in single cell RNA data.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular processes.
• Existing scRNA-seq analysis methods struggle with the continuous, coupled, and noisy nature of cellular differentiation dynamics.

Purpose of the Study:

• To introduce CODEVAE (Correlated Ordinary Differential Equation Variational Autoencoder), a novel deep generative framework.
• To address limitations in modeling continuous and coupled cellular dynamics in scRNA-seq data.
• To enhance the preservation of geometric continuity and biologically coupled variation.

Main Methods:

• CODEVAE integrates ordinary differential equation (ODE) constraints with correlation-aware latent representations.
• The framework builds upon a variational autoencoder, incorporating low-β regularization, an information bottleneck, ODE-based continuity, and correlated latent components.
• Evaluated using 18 metrics across 55 independent runs.

Main Results:

• CODEVAE demonstrated superior performance compared to advanced variational models, single-cell specific methods, graph/contrastive approaches, and traditional dimensionality reduction techniques.
• In multi-batch scenarios, CODEVAE maintained smooth data manifolds and improved integration quality.
• Successfully reconstructed a continuous megakaryocyte differentiation trajectory and identified stage-specific effects of Dapp1 perturbation in biological applications.

Conclusions:

• CODEVAE provides a robust and principled approach for modeling continuous cellular dynamics from scRNA-seq data.
• The framework facilitates the extraction of mechanistic insights across diverse single-cell contexts.
• CODEVAE represents a significant advancement in analyzing complex cellular trajectories.