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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53355-6 |
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| author | Zhenchao Tang Guanxing Chen Shouzhi Chen Jianhua Yao Linlin You Calvin Yu-Chian Chen |
| author_facet | Zhenchao Tang Guanxing Chen Shouzhi Chen Jianhua Yao Linlin You Calvin Yu-Chian Chen |
| author_sort | Zhenchao Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b703674671ed48bbaab75d65869ccdd1 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-b703674671ed48bbaab75d65869ccdd12025-08-20T02:17:47ZengNature PortfolioNature Communications2041-17232024-10-0115111510.1038/s41467-024-53355-6Modal-nexus auto-encoder for multi-modality cellular data integration and imputationZhenchao Tang0Guanxing Chen1Shouzhi Chen2Jianhua Yao3Linlin You4Calvin Yu-Chian Chen5Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityAI Lab, TencentArtificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen UniversityAI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate SchoolAbstract 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.https://doi.org/10.1038/s41467-024-53355-6 |
| spellingShingle | Zhenchao Tang Guanxing Chen Shouzhi Chen Jianhua Yao Linlin You Calvin Yu-Chian Chen Modal-nexus auto-encoder for multi-modality cellular data integration and imputation Nature Communications |
| title | Modal-nexus auto-encoder for multi-modality cellular data integration and imputation |
| title_full | Modal-nexus auto-encoder for multi-modality cellular data integration and imputation |
| title_fullStr | Modal-nexus auto-encoder for multi-modality cellular data integration and imputation |
| title_full_unstemmed | Modal-nexus auto-encoder for multi-modality cellular data integration and imputation |
| title_short | Modal-nexus auto-encoder for multi-modality cellular data integration and imputation |
| title_sort | modal nexus auto encoder for multi modality cellular data integration and imputation |
| url | https://doi.org/10.1038/s41467-024-53355-6 |
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