Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE

Alsu Missarova, Emma Dann, Leah Rosen, Rahul Satija, John Marioni

Research output: Contribution to journalArticlepeer-review

Abstract

Single-cell RNA-sequencing enables testing for differential expression (DE) between conditions at a cell type level. While powerful, one of the limitations of such approaches is that the sensitivity of DE testing is dictated by the sensitivity of clustering, which is often suboptimal. To overcome this, we present miloDE—a cluster-free framework for DE testing (available as an open-source R package). We illustrate the performance of miloDE on both simulated and real data. Using miloDE, we identify a transient hemogenic endothelia-like state in mouse embryos lacking Tal1 and detect distinct programs during macrophage activation in idiopathic pulmonary fibrosis.

Original languageEnglish (US)
Article number189
JournalGenome biology
Volume25
Issue number1
DOIs
StatePublished - Dec 2024

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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