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ACE: A Versatile Contrastive Learning Framework for Single-cell Mosaic Integration.

Xuhua Yan¹, Jinmiao Chen^2,3,4, Ruiqing Zheng¹

¹School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Genomics, Proteomics & Bioinformatics

|August 4, 2025

Summary

This summary is machine-generated.

Align and CompletE (ACE) is a new framework for integrating single-cell multi-omics data. It effectively handles varying data types and enhances the representation of cellular differences, even with incomplete data.

Keywords:

Contrastive learning Imputation Mosaic integration Multi-omics Single cell

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell multi-omics data integration is crucial for understanding cellular heterogeneity.
Mosaic integration presents challenges due to disparities in modality abundance across datasets.

Purpose of the Study:

To present Align and CompletE (ACE), a novel framework for mosaic integration of single-cell multi-omics data.
To address the challenge of modality abundance disparity in integrated datasets.

Main Methods:

ACE employs two strategies: modality alignment (ACE-align) and regression (ACE-spec).
ACE-align uses contrastive learning for explicit modality alignment and shared latent representations.
ACE-spec combines alignment and modality-specific representations for complete multi-omics data.

Main Results:

ACE demonstrates superior performance over existing methods in various mosaic integration scenarios.
ACE-spec effectively enhances the representation of cellular heterogeneity in datasets with incomplete modalities.
Experiments validated ACE's effectiveness in bi-modal and tri-modal integration.

Conclusions:

ACE provides a robust framework for mosaic integration of single-cell multi-omics data.
The ACE framework, particularly ACE-spec, improves the understanding of cellular heterogeneity from incomplete multi-omics datasets.