Xie Xueqin, Wu Changchun, Dao Fuying, Deng Kejun, Yan Dan, Huang Jian, Lyu Hao, Lin Hao
Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China.
School of Biological Sciences Nanyang Technological University Singapore Singapore.
Imeta. 2025 Jun 24;4(4):e70060. doi: 10.1002/imt2.70060. eCollection 2025 Aug.
scRiskCell is an interpretable intelligent computational framework that leverages nearly 500,000 islet cell expression profiles from 106 donors across different continuous disease states. By calculating the intrinsic relationship between donor disease states and cell expression profiles, it assigns a pseudo-cell state index to each cell. Sorting the pseudo-indexes of cells enables the identification of risk cells truly disrupted by the disease. Importantly, scRiskCell reveals the dynamic aggregation pattern of risk cells during disease progression, providing mechanistic insights for early disease prediction and clinical dynamic monitoring of disease progression.
scRiskCell是一个可解释的智能计算框架，它利用了来自106名不同疾病状态供体的近50万个胰岛细胞表达谱。通过计算供体疾病状态与细胞表达谱之间的内在关系，它为每个细胞分配一个伪细胞状态指数。对细胞的伪指数进行排序能够识别真正被疾病破坏的风险细胞。重要的是，scRiskCell揭示了疾病进展过程中风险细胞的动态聚集模式，为疾病早期预测和疾病进展的临床动态监测提供了机制性见解。
