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