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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "dawnn" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'dawnn: Differential Abundance with Neural Networks'
version: 2.2.0
doi: 10.1101/2023.05.05.539427
identifiers:
- type: doi
  value: 10.32614/CRAN.package.dawnn
abstract: 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) <https://doi.org/10.1101/2023.05.05.539427>.
authors:
- family-names: Hall
  given-names: George
  email: george.hall@ucl.ac.uk
  orcid: https://orcid.org/0000-0002-4828-0668
- family-names: Castellano
  given-names: Sergi
  email: s.castellano@ucl.ac.uk
  orcid: https://orcid.org/0000-0002-5819-4210
preferred-citation:
  type: article
  title: 'Dawnn: single-cell differential abundance with neural networks'
  authors:
  - family-names: Hall
    given-names: George T.
  - family-names: Castellano
    given-names: Sergi
    email: s.castellano@ucl.ac.uk
    orcid: https://orcid.org/0000-0002-5819-4210
  journal: bioRxiv
  year: '2023'
  doi: 10.1101/2023.05.05.539427
  url: https://www.biorxiv.org/content/10.1101/2023.05.05.539427v2
  notes: Preprint
repository: https://george-hall-ucl.r-universe.dev
repository-code: https://github.com/george-hall-ucl/dawnn
commit: e809d74546faf5c95664c07d030a07e51fae1921
url: https://george-hall-ucl.github.io/dawnn/
date-released: '2026-09-28'
contact:
- family-names: Hall
  given-names: George
  email: george.hall@ucl.ac.uk
  orcid: https://orcid.org/0000-0002-4828-0668