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# CITATION 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 "densvis" in publications use:'
type: software
license: MIT
title: 'densvis: Density-Preserving Data Visualization via Non-Linear Dimensionality
  Reduction'
version: 1.15.0
doi: 10.1038/s41587-020-00801-7
identifiers:
- type: doi
  value: 10.32614/CRAN.package.densvis
abstract: Implements the density-preserving modification to t-SNE and UMAP described
  by Narayan et al. (2020) <https://doi.org/10.1101/2020.05.12.077776>. The non-linear
  dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex
  high-dimensional sequencing data such as single cell RNAseq using lower dimensional
  representations. These lower dimensional representations enable the visualisation
  of discrete transcriptional states, as well as continuous trajectory (for example,
  in early development). However, these methods focus on the local neighbourhood structure
  of the data. In some cases, this results in misleading visualisations, where the
  density of cells in the low-dimensional embedding does not represent the transcriptional
  heterogeneity of data in the original high-dimensional space. den-SNE and densMAP
  aim to enable more accurate visual interpretation of high-dimensional datasets by
  producing lower-dimensional embeddings that accurately represent the heterogeneity
  of the original high-dimensional space, enabling the identification of homogeneous
  and heterogeneous cell states. This accuracy is accomplished by including in the
  optimisation process a term which considers the local density of points in the original
  high-dimensional space. This can help to create visualisations that are more representative
  of heterogeneity in the original high-dimensional space.
authors:
- family-names: O'Callaghan
  given-names: Alan
  email: alan.ocallaghan@outlook.com
- family-names: Narayan
  given-names: Ashwinn
- family-names: Cho
  given-names: Hyunghoon
preferred-citation:
  type: article
  title: Assessing single-cell transcriptomic variability through density-preserving
    data visualization
  authors:
  - name: Narayan
  - name: Ashwin
  - name: Berger
  - name: Bonnie
  - name: Cho
  - name: Hyunghoon
  journal: Nature Biotechnology
  year: '2021'
  doi: 10.1038/s41587-020-00801-7
repository: https://bioc.r-universe.dev
repository-code: https://github.com/Alanocallaghan/densvis
commit: 92f82880f4e3492ed1c790095e8f14549adb5307
url: https://bioconductor.org/packages/densvis
date-released: '2024-03-05'
contact:
- family-names: O'Callaghan
  given-names: Alan
  email: alan.ocallaghan@outlook.com