In this video we explain how Flow Cytometry generates High Dimensional datasets and we overview the statistical techniques that are used to visualise them.
We explain the difference between linear and non-linear dimension reduction and why non-linear reduction discovers Flow Cytometry population clusters better.
We will look at how dimension reduction techniques can deform data, and we recommend UMAP as the most modern technique to apply in high dimensional flow cytometry analysis.
Table of Contents:
00:00 - Introduction
00:17 - What is High Dimensional data
00:58 - Overview Dimension Reduction Techniques
02:53 - Data deformation experiment
05:20 - UMAP is recommended