Tutorial: Make a tSNE Plot in FlowJo with Flow Cytometry Data
This tutorial describes how to use tSNE to analyze flow cytometry data in FlowJo, and also teaches advanced tSNE visualizations.
This tutorial describes how to use tSNE to analyze flow cytometry data in FlowJo, and also teaches advanced tSNE visualizations.
No matter what cell type you’re interested in exploring with flow cytometry, the majority of analyses start with the same basic gating strategy.
t-SNE is an algorithm used for arranging high-dimensional data points in a two-dimensional space so that events which are highly related by many variables are most likely to aggregate closely together.
Fluorochromes are critical components of flow cytometry experiments and rely heavily on the principles of fluorescence in order to function. In this post I’ll explain how fluorescence works, and then dive into fluorochromes and flow cytometry.
In Case Study 1 Part 2, I explain the process of data normalization, or aligning peaks of data to make gating quicker and easier, as well as make the data suitable for clustering analyses.
In Case Study 1 Part 1 I use FCS files publicly available from FlowRepository to go through a detailed analysis of a dataset, from data cleaning to sophisticated cluster-based data extraction.
Flow cytometry dot plots need to be optimized for grants and publications, but the way you choose to display the plots can affect the clarity and purpose of the data. Finding the best resolution for your flow cytometry plots ensures accuracy and meaningfulness of the data.