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

Zhenchao Tang, Guanxing Chen, Shouzhi Chen, Jianhua Yao (), Linlin You () and Calvin Yu-Chian Chen ()
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Zhenchao Tang: Shenzhen Campus of Sun Yat-sen University
Guanxing Chen: Shenzhen Campus of Sun Yat-sen University
Shouzhi Chen: Shenzhen Campus of Sun Yat-sen University
Jianhua Yao: Tencent
Linlin You: Shenzhen Campus of Sun Yat-sen University
Calvin Yu-Chian Chen: Peking University Shenzhen Graduate School

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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-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.

Date: 2024
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DOI: 10.1038/s41467-024-53355-6

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