Package: densvis
Title: Density-Preserving Data Visualization via Non-Linear
        Dimensionality Reduction
Version: 1.22.0
Date: 2025-11-04
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
Config/pak/sysreqs: libpng-dev python3
Repository: https://bioc-release.r-universe.dev
Date/Publication: 2026-04-28 12:54:19 UTC
RemoteUrl: https://github.com/bioc/densvis
RemoteRef: RELEASE_3_23
RemoteSha: 32fc121f5ed64aa4dd845aa512b3eae87ccf3f65
NeedsCompilation: yes
Packaged: 2026-04-29 21:13:02 UTC; root
Author: Alan O'Callaghan [aut, cre],
  Ashwinn Narayan [aut],
  Hyunghoon Cho [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
Built: R 4.6.0; x86_64-w64-mingw32; 2026-04-29 21:17:48 UTC; windows
Archs: x64
