scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

作者：Xiuhui Yang, Koren K. Mann, Hao Wu, Jun Ding · 发表于：Genome biology · 年份：2024 · DOI：10.1186/s13059-024-03338-z · 被引用次数：80 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Gene Regulatory Network Analysis

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.