Package: densvis
Title: Density-Preserving Data Visualization via Non-Linear
        Dimensionality Reduction
Version: 1.19.0
Date: 2025-07-17
Authors@R: 
  c(
    person(
      given = "Alan",
      family = "O'Callaghan",
      role = c("aut", "cre"),
      email = "alan.ocallaghan@outlook.com"
    ),
    person(given = "Ashwinn", family = "Narayan", role = "aut"),
    person(given = "Hyunghoon", family = "Cho", role = "aut")
  )
Description: 
  Implements the density-preserving modification to t-SNE 
  and UMAP described by Narayan et al. (2020) 
  <doi: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.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba
Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat
BugReports: https://github.com/Alanocallaghan/densvis/issues
LinkingTo: Rcpp
biocViews: DimensionReduction, Visualization, Software, SingleCell,
        Sequencing
VignetteBuilder: knitr
URL: https://bioconductor.org/packages/densvis
StagedInstall: no
git_url: https://git.bioconductor.org/packages/densvis
git_branch: devel
git_last_commit: 7d2a35c
git_last_commit_date: 2025-07-17
Repository: Bioconductor 3.22
Date/Publication: 2025-07-17
NeedsCompilation: yes
Packaged: 2025-07-17 22:38:40 UTC; biocbuild
Author: Alan O'Callaghan [aut, cre],
  Ashwinn Narayan [aut],
  Hyunghoon Cho [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
Built: R 4.5.1; x86_64-w64-mingw32; 2025-07-18 12:49:03 UTC; windows
Archs: x64
