Differential abundance testing on single-cell data using k-nearest neighbor graphs
Differential abundance testing on single-cell data using k-nearest neighbor graphs
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DOI：
10.1038/s41587-021-01033-z
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发表时间：
2021-09-30
影响因子：
46.9
通讯作者：
Marioni, John C.
中科院分区：
文献类型：
--
作者：
Dann, Emma;Henderson, Neil C.;Marioni, John C.
目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的集群作为输入。然而，星系团并不总是提供适当的分辨率，也不能捕捉到连续的轨迹。在这里，我们介绍了MALO，一个可扩展的统计框架，通过将细胞分配到k最近邻图上部分重叠的邻域来执行差异丰度测试。使用模拟和单细胞RNA测序(scRNA-seq)数据，我们证明了MILO可以识别通过将细胞离散成簇而被遮挡的扰动，它保持了对批量效应的错误发现率控制，并且它的表现优于其他差异丰度测试策略。在老化的小鼠胸腺中，Milo发现了一种偏向于命运的上皮前体的衰退，并发现了人类肝硬变中多个谱系的扰动。由于Milo基于细胞-细胞相似性结构，因此它也可能适用于scRNA-seq以外的单细胞数据。Https://github.com/MarioniLab/miloR.上以开源R软件包的形式提供了MALO
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.