Preservation of Global Structure in Uniform Manifold Approximation and Projection (UMAP) • UMAPgp

Introduction

Uniform Manifold Approximation and Projection (UMAP) preserves local structure by grouping neighboring data points together which provides an informative visualization of heterogeneity in a dataset. However, if one wants to understand the relationship between clusters of data points, this can be troublesome since UMAP does not warrant that inter-cluster distances are preserved correctly. To address this shortcoming, we have created a package, UMAPgp which preserves the global structure of manifold learning algorithms without losing the finer details. The developed package is suited to process small datasets with approximately 5,000 data points.

Installation of the package

Below is a procedure for the UMAPgp package installation:-

$ library(devtools)
$ install_github("https://github.com/BNgigi/UMAPgp-R-Package")
$ library(UMAPgp)

Link to the package vignette

Here-in is the link to UMAPgp Package Vignette:-

Package Vignette

Example code block

Use the package as guided below:-

#### Usage
umapgp(var, data)
var: A variable of interest from the dataframe, it should be categorical.
data: The dataset to be used for analysis, it should be a dataframe with no rownames.
#### Example
library(UMAPgp)
data("iris")
umapgp(var="Species", data=iris)