Differential abundance testing on single-cell data using K-nearest neighbour graphs - Dann, Emma (Nat Biotechnol, 2022)
Current computational workflows for comparative analyses of single cell datasets typically use discrete clusters as input when testing for differential abundance between 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 neighbourhoods on a k-nearest neighbour graph. Using simulations and scRNA-seq data, we show that Milo can identify perturbations that are obscured by discretising cells into clusters, that it maintains FDR 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 ageing mouse thymus, and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell-cell-similarity structure, it may 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.