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  "Date": "2025-11-04",
  "Authors@R": "c(\nperson(\ngiven = \"Alan\",\nfamily = \"O'Callaghan\",\nrole = c(\"aut\", \"cre\"),\nemail = \"alan.ocallaghan@outlook.com\"\n),\nperson(given = \"Ashwinn\", family = \"Narayan\", role = \"aut\"),\nperson(given = \"Hyunghoon\", family = \"Cho\", role = \"aut\")\n)",
  "Description": "Implements the density-preserving modification to t-SNE\nand UMAP described by Narayan et al. (2020)\n<doi:10.1101/2020.05.12.077776>. The non-linear dimensionality\nreduction techniques t-SNE and UMAP enable users to summarise\ncomplex high-dimensional sequencing data such as single cell\nRNAseq using lower dimensional representations. These lower\ndimensional representations enable the visualisation of\ndiscrete transcriptional states, as well as continuous\ntrajectory (for example, in early development). However, these\nmethods focus on the local neighbourhood structure of the data.\nIn some cases, this results in misleading visualisations, where\nthe density of cells in the low-dimensional embedding does not\nrepresent the transcriptional heterogeneity of data in the\noriginal high-dimensional space. den-SNE and densMAP aim to\nenable more accurate visual interpretation of high-dimensional\ndatasets by producing lower-dimensional embeddings that\naccurately represent the heterogeneity of the original\nhigh-dimensional space, enabling the identification of\nhomogeneous and heterogeneous cell states. This accuracy is\naccomplished by including in the optimisation process a term\nwhich considers the local density of points in the original\nhigh-dimensional space. This can help to create visualisations\nthat are more representative of heterogeneity in the original\nhigh-dimensional space.",
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