Why you should be using PHATE for dimensionality reduction

As data scientists, we often work with high-dimensional data with more than 3 features, or dimensions, of interest. In supervised machine learning, we may use this data for training and classification for example and may reduce the dimensions to speed up the training. In unsupervised learning, we use this type of data for visualization and clustering. In single-cell RNA sequencing (scRNA-seq), for example, we accumulate measurements of tens of thousands of genes per cell for upwards of a million cells. That's a lot of data that provides a window into the cell's identity, state, and other properties.