Modal-nexus auto-encoder for multi-modality cellular data integration and imputation.

ABSTRACT: Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there's a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-m

SUBMITTER: Tang Z 

PROVIDER: S-EPMC11489673 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature