Authors: Sean Hughes, Angela Li, Ju Kim, Malisa Smith, Ted Laderas
This post describes a project from rOpenSci unconf18. In the spirit of exploration and experimentation at our unconferences, projects are not necessarily finished products or in scope for rOpenSci packages.
A few weeks ago, as part of the rOpenSci Unconference, a group of us decided to work on making the UMAP algorithm accessible within R. UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique that allows the user to reduce high dimensional data (multiple columns) into a smaller number of columns for visualization purposes. It is similar to both Principal Components Analysis (PCA) and t-SNE, which are techniques often used in the single-cell omics (such as genomics, flow cytometry, proteomics) world to visualize high dimensional data.
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Read the full post about the umapr package, including profiling runtime and memory use on different datasets as well as a Shiny app: https://ropensci.org/blog/2018/08/01/umapr/