A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
How cell clusters are defined in single-cell sequencing data has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. Here, the authors present a new approach that enables the prediction of differentially expressed genes without relying...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-08-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17900-3 |