r-bioc-densvis - 1.12.1+dfsg-1 main

Implements the density-preserving modification to t-SNE
and UMAP described by Narayan et al. (2020)
.
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.