细胞状态转换可以在时间分辨的生物现象中揭示单细胞核糖核酸(RNA)测序数据的附加信息。然而，大多数当前方法是基于基因表达状态的时间导数，这限制了它们对细胞状态短期演变的研究。在这里，我们提出了单细胞RNA测序(scRNA-seq)数据的状态转换跨样品(scSTAR)方法，通过使用偏最小二乘和最小二乘误差方法，构建生物条件之间的成对细胞投影，以最大化两个特征空间之间的协方差，从而克服了这一限制。在老鼠衰老数据中，发现CD4+记忆T细胞亚型对应应激反应与衰老相关。识别了一种mTORC活化的新型Treg亚型与抗肿瘤免疫抑制相关，这得到了来自The Cancer Genome Atlas计划的11种癌症免疫荧光显微镜和生存分析的证实。在黑色素瘤数据中，scSTAR将免疫疗法反应预测准确度从0.8提高到0.96。©作者(们) 2023年。由牛津大学出版社出版。版权所有。请发送电子邮件至journals.permissions@oup.com以获得授权。

Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.