小柯机器人

英国威康桑格研究所John C. Marioni、剑桥大学Michael D. Morgan等研究人员合作使用k-近邻图对单细胞数据进行差异丰度测试。该项研究成果于2021年9月30日在线发表在《自然—生物技术》杂志上。

研究人员提出了Milo，一个可扩展的统计框架，通过将细胞分配到k-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，研究人员表明Milo可以识别因将细胞离散成集群而被掩盖的扰动，它在批次效应中保持了错误发现率控制，并且它优于其他差异丰度测试策略。Milo识别了衰老小鼠胸腺中命运偏向的上皮前体细胞衰退，并识别了人类肝硬化中多系细胞的扰动情况。

由于Milo是基于细胞-细胞相似性结构，它也可能适用于scRNA-seq以外的单细胞数据。Milo作为一个开源的R软件包提供，网址是https://github.com/MarioniLab/miloR。

据介绍，目前对单细胞数据集进行比较分析的计算工作流程，在测试不同实验条件下的丰度差异时，通常使用离散的聚类作为输入。然而，集群并不总是提供适当的分辨率，也不能捕捉连续的轨迹。

附：英文原文

Title: Differential abundance testing on single-cell data using k-nearest neighbor graphs

Author: Dann, Emma, Henderson, Neil C., Teichmann, Sarah A., Morgan, Michael D., Marioni, John C.

Issue&Volume: 2021-09-30

Abstract: 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

DOI: 10.1038/s41587-021-01033-z

Source: https://www.nature.com/articles/s41587-021-01033-z

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