Package: dawnn 2.2.0

dawnn: Differential Abundance with Neural Networks

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>.

Authors:George Hall [aut, cre], Sergi Castellano [aut], University College London [cph]

dawnn_2.2.0.tar.gz
dawnn_2.2.0.zip(r-4.7-any)dawnn_2.2.0.zip(r-4.6-any)dawnn_2.2.0.zip(r-4.5-any)
dawnn_2.2.0.tgz(r-4.6-any)dawnn_2.2.0.tgz(r-4.5-any)
dawnn_2.2.0.tar.gz(r-4.7-any)dawnn_2.2.0.tar.gz(r-4.6-any)
dawnn_2.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dawnn/json (API)

# Install 'dawnn' in R:

install.packages('dawnn', repos = c('https://george-hall-ucl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/george-hall-ucl/dawnn/issues

Pkgdown/docs site:https://george-hall-ucl.github.io

On CRAN:

4.74 score 2 stars 11 scripts 396 downloads 2 exports 153 dependencies

Last updated from:e809d74546. Checks:9 OK. Indexed: yes.

Target	Result	Time	Files	Syslog
linux-devel	OK	260
source / vignettes	OK	264
linux-release	OK	270
macos-release	OK	323
macos-oldrel	OK	168
windows-devel	OK	178
windows-release	OK	187
windows-oldrel	OK	189
wasm-release	OK	190

Exports:download_model run_dawnn

Dependencies:abind askpass backports base64enc BH bitops bslib cachem caTools cli cluster codetools commonmark config cowplot cpp11 crosstalk curl data.table deldir digest dotCall64 dplyr dqrng evaluate farver fastDummies fastmap fitdistrplus FNN fontawesome fs future future.apply generics ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph irlba isoband jquerylib jsonlite keras KernSmooth knitr labeling later lattice lazyeval lifecycle listenv lmtest magrittr MASS Matrix matrixStats memoise mime miniUI nlme openssl otel parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip processx progressr promises ps purrr R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra rstudioapi Rtsne S7 sass scales scattermore sctransform Seurat SeuratObject shiny sitmo sourcetools sp spam spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr survival sys tensor tensorflow tfautograph tfruns tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite whisker withr xfun xtable yaml zeallot zoo

Installation | Main workflow | Required parameters | Output | Optional parameters | Changing model location | Downloading a model from another location | SessionInfo

Rendered fromdawnn.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2023-03-30

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Download the neural network model used by Dawnn.	download_model
Identify which cells are in regions of differential abundance using Dawnn.	run_dawnn

Usage by other packages (reverse dependencies)