Differential abundance testing on single-cell data using K-nearest neighbour graphs

By E. Dann, N. Henderson, Sarah A. Teichmann, Martin T. Morgan, John C. Marioni. Nature Biotechnology, 2021

678 citations recorded by the source corpus.

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. Milo identifies differentially abundant populations of cells in scRNA-seq data without clustering.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8ec438059055824d97a6e0d68ad91a284f526b8c

Citations in the corpus