小柯机器人

美国哈佛医学院Soumya Raychaudhuri团队利用共变邻域分析揭示与单细胞转录组学中感兴趣的表型相关细胞群。这一研究成果于2021年10月21日在线发表在国际学术期刊《自然—生物技术》上。

研究人员提出了共变邻域分析（CNA），这是一种无偏倚的方法，可用于识别相关的细胞群，比基于集群的方法具有更大的灵活性。CNA通过识别转录空间中的小区域组（被称为邻域）在不同样本中的丰度共同变化，来描述了不同样本的主导变化轴，并表明有共同的功能或调节。CNA对任何样本级属性和这些共同变化的邻域组丰度之间的关联进行了统计测试。模拟结果显示，与基于集群的方法相比，CNA能够更敏感和准确地识别疾病相关的细胞状态。当应用于已发表的数据集时，CNA捕获了类风湿关节炎中的Notch激活特征，识别了败血症中扩大的单核细胞群，并识别了与活动性肺结核进展相关的新型T细胞群。

据介绍，随着单细胞数据集样本量的增长，迫切需要对不同样本的细胞状态进行描述，并与样本属性（如临床表型）相关。目前的统计方法通常将细胞映射到集群中，然后评估集群丰度的差异。

附：英文原文

Title: Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics

Author: Reshef, Yakir A., Rumker, Laurie, Kang, Joyce B., Nathan, Aparna, Korsunsky, Ilya, Asgari, Samira, Murray, Megan B., Moody, D. Branch, Raychaudhuri, Soumya

Issue&Volume: 2021-10-21

Abstract: As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space—termed neighborhoods—that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

DOI: 10.1038/s41587-021-01066-4

Source: https://www.nature.com/articles/s41587-021-01066-4

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex