Visualization of very large high-dimensional data sets as minimum spanning trees.

ABSTRACT: The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree (http://tmap.gdb.tools). Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structur

SUBMITTER: Probst D 

PROVIDER: S-EPMC7015965 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature