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

Author:

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

Abstract

AbstractSingle-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.

Funder

National Institute for Occupational Safety and Health

Chan Zuckerberg Initiative

Wellcome Trust

European Bioinformatics Institute

EMBL´s European Bioinformatics Institute (EMBL-EBI)

Publisher

Springer Science and Business Media LLC

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