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

Zhenchao Tang

doi:10.1038/s41467-024-53355-6

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

2024-10-18

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OA

AI

Calvin Yu‐Chian Chen *

DOI：10.1038/s41467-024-53355-6

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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.

Keywords:

EXPRESSION

PROLIFERATION

4-ALPHA

GENES

CELLS

RNA

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

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