CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data

TL;DR

CAVACHON is a hierarchical variational autoencoder designed to integrate multi-modal single-cell data by explicitly modeling biological relationships, improving data interpretation and enabling diverse analyses in single-cell multi-omics research.

Contribution

It introduces a novel probabilistic framework that incorporates prior biological relationships via a directed acyclic graph into a hierarchical variational autoencoder for single-cell data integration.

Findings

Effective separation of shared and modality-specific information

Enhanced differential analysis across modalities

Improved cell clustering accuracy

Abstract

Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological relationships between the data modalities, which could significantly enhance modeling and interpretation. We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data as a directed acyclic graph using a generalized hierarchical variational autoencoder. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration. These include the isolation of common and distinct information from different modalities, modality-specific differential analysis, and integrated cell clustering. We anticipate that the proposed framework can facilitate the construction of highly flexible graphical models that can capture the complexities of biological hypotheses and unravel the connections between different biological data types, such as different modalities of paired single-cell multi-omics data. The implementation of the proposed framework can be found in the repository https://github.com/kuijjerlab/CAVACHON.