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深度学习在单细胞基因组学与转录组学数据分析中的应用

文章系统回顾了深度学习技术在单细胞测序分析中的最新进展，涵盖基因组学、转录组学、空间转录组学以及多组学整合等多个方向。单细胞技术的出现，使研究者能够在单细胞层面解析细胞异质性与分化动态，从而揭示复杂生物系统的组织规律与疾病发生机制。

Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE . Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions.

68 种单细胞批次整合方法的比较，作者附上了分析的代码，做大规模数据库挖掘的同学好好学习一下

Advanced Science｜吉大/川大/南方医大联合：AI筛选甘草酸/辛伐他汀自组装纳米晶，破解骨髓炎感染-炎症-骨损三重死结

AI 精准筛选甘草酸 / 辛伐他汀自组装纳米晶，一药打通杀菌、抗炎、骨修复全链条，破解细菌性骨髓炎临床治疗困局。