ArticleNature biotechnology2022
Differential abundance testing on single-cell data using k-nearest neighbor graphs.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 553 papers, 1 of them a synthesis that pooled it.
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Who cites it
553 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Periventricular gradient pathology is linked to ependymal reactivity and cell adhesion disruption in multiple sclerosis.Nature neuroscience · 2026Article
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- Very Long Chain Fatty Acid Synthesis is a Targetable Dependency in RAS Pathway Mutant Leukemia.Blood cancer discovery · 2026Article
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- Single-cell immune repertoire atlas maps coordinated circulating adaptive immune states in inflammatory bowel disease.bioRxiv : the preprint server for biology · 2026Article
- Cysteine supplementation reverses immune dysfunction in cancer patients with severe COVID-19.medRxiv : the preprint server for health sciences · 2026Article
- Folate deficiency disrupts key metabolic transitions within the developing neural ectoderm.bioRxiv : the preprint server for biology · 2026Article
- The EF1α promoter of cilta-cel drives a highly effective GZMKNature communications · 2026Article
493 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
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 .
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