PDF(Pubmed)
Abstract：
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.
摘要：
单细胞多组学数据揭示了复杂的细胞状态，提供对细胞动力学和疾病的重要见解。然而，多组数据的整合带来了挑战。一些模式尚未达到已建立的转录组学的稳健性或清晰度。再加上不太成熟的模式和集成复杂性的数据稀缺，这些挑战限制了我们最大化单细胞组学益处的能力.我们介绍scCross,一种利用变量自动编码器的工具，生成对抗网络，以及用于模态对齐的相互最近邻(MNN)技术。通过启用单细胞跨模态数据生成，多组数据模拟，在硅细胞扰动中，scCross增强了单细胞多组学研究的实用性。
