Implements the densitypreserving modification to tSNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. The nonlinear dimensionality reduction techniques tSNE and UMAP enable users to summarise complex highdimensional 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 lowdimensional embedding does not represent the transcriptional heterogeneity of data in the original highdimensional space. denSNE and densMAP aim to enable more accurate visual interpretation of highdimensional datasets by producing lowerdimensional embeddings that accurately represent the heterogeneity of the original highdimensional 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 highdimensional space. This can help to create visualisations that are more representative of heterogeneity in the original highdimensional space.
Package details 


Author  Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] 
Bioconductor views  DimensionReduction Sequencing SingleCell Software Visualization 
Maintainer  Alan O'Callaghan <alan.ocallaghan@outlook.com> 
License  MIT + file LICENSE 
Version  1.00.6 
Package repository  View on Bioconductor 
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