CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints
Chen, Y.; Qi, C.; Fang, H.; Luan, F.; Zhang, Z.; Arya, S.; Wei, Z.
Show abstract
Intercellular communication is a fundamental driver of cellular identity and tissue homeostasis, yet current single-cell representation learning frameworks predominantly model cell state as a function of intrinsic gene expression, neglecting the extrinsic signaling context. Conversely, dedicated communication inference tools are often constrained by the sparsity and noise of raw transcriptomic data. Here, we present CoLa-VAE, a deep generative framework that explicitly integrates cell-cell communication (CCC) constraints into latent variable learning. By employing a dynamic graph Laplacian regularization derived from pairwise ligand-receptor interactions, CoLa-VAE disentangles communication-driven topology from intrinsic transcriptional heterogeneity within a variational autoencoder architecture. We demonstrate that CoLa-VAE serves as a robust, method-agnostic framework compatible with diverse signaling definitions, consistently outperforming state-of-the-art baselines in structural clustering metrics and denoising fidelity across heterogeneous sequencing platforms.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- scDREAMER: atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier 98%
- OmicVerse: A single pipeline for exploring the entire transcriptome universe 97%
- scConfluence : single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features 97%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.