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

ABSTRACT: The integration of single-cell multi-omics datasets is critical for deciphering cellular heterogeneities. Mosaic integration, the most general integration task, poses a greater challenge regarding disparity in modality abundance across datasets. Here, we present Align and CompletE (ACE), a mosaic integration framework that assembles two types of strategies to handle this problem: modality alignment-based strategy (ACE-align) and regression-based strategy (ACE-spec). ACE-align utilizes a novel contrastive learning objective for explicit modality alignment to uncover the shared latent representations behind modalities. ACE-spec combines the modality alignment results and modality-specific representations to construct complete multi-omics representations for all datasets. Extensive experiment

SUBMITTER: Yan X 

PROVIDER: S-EPMC12582371 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature