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

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