Calvin Yu-Chian Chen

Oct 18, 2024 | nature.com | Guanxing Chen |Jianhua Yao |Linlin You |Calvin Yu-Chian Chen |Zhenchao Tang |Shouzhi Chen

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-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E’s accuracy and robustness in multi-modality cellular data integration and imputation. Heterogeneous feature spaces and technical noise hinder the cellular data integration and further analysis. Here, authors report a Modal-Nexus Auto-Encoder (Monae) to effectively integrate unpaired multi-modality cellular data and generate imputation counts for downstream analysis.