Differential Abundance with Neural Networks R package dawnn version 2.2.0 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>. Software Depends: R (>= 4.0.0) Imports: stats, Seurat, reticulate, keras, utils, withr, tools Suggests: rmarkdown, knitr, testthat (>= 3.1.7), callr, dplyr, pkgload, viridis George Hall <george.hall@ucl.ac.uk> Comprehensive R Archive Network (CRAN) George Hall [aut, cre] (ORCID: <https://orcid.org/0000-0002-4828-0668>), Sergi Castellano [aut] (ORCID: <https://orcid.org/0000-0002-5819-4210>), University College London [cph] GPL (>= 3) 2026-09-07 application/tgz https://CRAN.R-project.org/package=dawnn doi:10.32614/CRAN.package.dawnn