Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a k-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell–cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at https://github.com/MarioniLab/miloR.

当前用于单细胞数据集比较分析的计算工作流程在测试实验条件之间的差异丰度时通常使用离散簇作为输入。然而，集群并不总是提供适当的分辨率，也无法捕捉到连续的轨迹。在这里，我们介绍了 Milo，这是一个可扩展的统计框架，它通过将单元分配到k上的部分重叠邻域来执行差异丰度测试-最近邻图。使用模拟和单细胞 RNA 测序 (scRNA-seq) 数据，我们表明 Milo 可以识别通过将细胞离散成簇而被掩盖的扰动，它保持跨批次效应的错误发现率控制，并且它优于替代差异丰度测试策略. Milo 确定了衰老小鼠胸腺中一种命运偏向的上皮前体的衰退，并确定了人类肝硬化肝脏中多个谱系的扰动。由于 Milo 基于细胞间相似性结构，因此它也可能适用于 scRNA-seq 以外的单细胞数据。Milo 在 https://github.com/MarioniLab/miloR 作为开源 R 软件包提供。