Joint analysis of multi-omic single-cell data across cohorts has
significantly enhanced the comprehensive analysis of cellular processes.
However, most of the existing approaches for this purpose require access to
samples with complete modality availability, which is impractical in many
real-world scenarios. In this paper, we propose (Single-Cell Cross-Cohort
Cross-Category) integration, a novel framework that learns unified cell
representations under domain shift without requiring full-modality reference
samples. Our generative approach learns rich cross-modal and cross-domain
relationships that enable imputation of these missing modalities. Through
experiments on real-world multi-omic datasets, we demonstrate that offers a
robust solution to single-cell tasks such as cell type clustering, cell type
classification, and feature imputation.

跨群体整合多组学单细胞数据的新方法，通过学习统一细胞表达，实现无需全模态参考样本的跨域关系和特征填充，为单细胞聚类、分类和特征填充等任务提供了稳健的解决方案。