Package

r-densvis

Description

Density-Preserving Data Visualization via Non-Linear Dimensionality
Reduction. 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.

Note