Detects regions of differential abundance in single-cell
transcriptomic data by applying a pre-trained neural network model to the
labels of each cell's nearest neighbours. Tests for both local and global
differential abundance, controlling the false discovery rate with the
Benjamini-Yekutieli procedure. The method is described in Hall and
Castellano (2023) <doi:10.1101/2023.05.05.539427>.
| Version: |
2.1.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
stats, Seurat, reticulate, keras, utils, withr, tools |
| Suggests: |
rmarkdown, knitr, testthat (≥ 3.1.7), callr, dplyr, pkgload, viridis |
| Published: |
2026-09-01 |
| DOI: |
10.32614/CRAN.package.dawnn (may not be active yet) |
| Author: |
George Hall  [aut,
cre],
Sergi Castellano
[aut],
University College London [cph] |
| Maintainer: |
George Hall <george.hall at ucl.ac.uk> |
| BugReports: |
https://github.com/george-hall-ucl/dawnn/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://george-hall-ucl.github.io/dawnn/,
https://github.com/george-hall-ucl/dawnn |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.9) with TensorFlow (>= 2.0), typically
installed in a conda environment and selected via the
'tf_conda_env' argument of run_dawnn(). See the package
documentation for setup instructions. |
| Citation: |
dawnn citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
dawnn results |