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

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

doi:10.1186/s13059-024-03334-3

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

Genome Biol. 2024 Jul 18;25(1):189. doi: 10.1186/s13059-024-03334-3.

Authors

Alsu Missarova^{1

2}, Emma Dann³, Leah Rosen^{1

2}, Rahul Satija^{4

5}, John Marioni^{6

7}

Affiliations

¹ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
² Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
³ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
⁴ Center for Genomics and Systems Biology, NYU, New York, USA. [email protected].
⁵ New York Genome Center, New York, USA. [email protected].
⁶ European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK. [email protected].
⁷ Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. [email protected].

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.

MeSH terms

Animals
Gene Expression Profiling
Humans
Idiopathic Pulmonary Fibrosis / genetics
Mice
Sequence Analysis, RNA
Single-Cell Analysis* / methods
Software

Abstract

MeSH terms

Grants and funding