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.

随着单细胞数据集样本量的增加，迫切需要表征细胞状态，这些细胞状态因样本而异并与样本属性（如临床表型）相关联。当前的统计方法通常将细胞映射到簇，然后评估簇丰度的差异。在这里，我们提出了共变邻域分析 (CNA)，这是一种比基于聚类的方法更灵活地识别相关细胞群的无偏方法。CNA 通过识别转录空间中的小区域组（称为邻域）来表征样本间变异的主导轴，这些小区域在样本间大量共同变化，表明共享功能或监管。CNA 对任何样本级属性与这些共同变化的社区群体的丰度之间的关联进行统计测试。模拟表明，与基于集群的方法相比，CNA 能够更灵敏、更准确地识别疾病相关的细胞状态。当应用于已发布的数据集时，CNA 捕获类风湿性关节炎中的 Notch 激活特征，识别脓毒症中扩增的单核细胞群，并识别与进展为活动性结核病相关的新型 T 细胞群。