Package: dawnn Title: Differential Abundance with Neural Networks Version: 2.2.0 Authors@R: c(person(given = "George", family = "Hall", role = c("aut", "cre"), email = "george.hall@ucl.ac.uk", comment = c(ORCID = "0000-0002-4828-0668")), person(given = "Sergi", family = "Castellano", role = c("aut"), email = "s.castellano@ucl.ac.uk", comment = c(ORCID = "0000-0002-5819-4210")), person(given = "University College London", role = c("cph"))) Description: 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) . License: GPL (>= 3) Encoding: UTF-8 Roxygen: list(markdown = TRUE) Imports: stats, Seurat, reticulate, keras, utils, withr, tools Depends: R (>= 4.0.0) Suggests: rmarkdown, knitr, testthat (>= 3.1.7), callr, dplyr, pkgload, viridis Config/testthat/edition: 3 VignetteBuilder: knitr URL: https://george-hall-ucl.github.io/dawnn/, https://github.com/george-hall-ucl/dawnn BugReports: https://github.com/george-hall-ucl/dawnn/issues 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. Config/roxygen2/version: 8.0.0 RoxygenNote: 7.3.3 Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev python3 zlib1g-dev Repository: https://george-hall-ucl.r-universe.dev Date/Publication: 2026-09-28 16:13:51 UTC RemoteUrl: https://github.com/george-hall-ucl/dawnn RemoteRef: HEAD RemoteSha: e809d74546faf5c95664c07d030a07e51fae1921 NeedsCompilation: no Packaged: 2026-09-28 16:33:19 UTC; root Author: George Hall [aut, cre] (ORCID: ), Sergi Castellano [aut] (ORCID: ), University College London [cph] Maintainer: George Hall