tSNE for Flow Cytometry: Theory and Practice

Learn how t-SNE is an invaluable tool for analyzing your flow cytometry data. As flow cytometry moves increasingly towards high-parameter datasets, dimensionality reduction algorithms like t-SNE become invaluable for teasing out insights from your data. This talk will discuss the theory behind t-SNE, why and when it’s useful for flow cytometry, and the practical steps of running the algorithm and interpreting the results. Proteintech Group welcomes Robert Ladd, Facility Manager at Loyola Medical Centre Flow Cytometry Core as he covers the theory and practice of t-SNE for flow cytometry. Learn how to get the most out of your Flow Cytometry data by utilizing t-SNE plots, and how to produce and interpret your data. 00:00 - Introduction to Proteintech 02:00 - t-SNE: An introduction 11:29 - The Bigger Picture 12:49 - Measuring and normalising high-dimensional similarities 14:54 - Project datapoints onto low-dimensional space 16:39 - Iteratively adjust low-dimensional distributions 18:39 - Practice 19:38 - Pre-processing 24:08 - Processing 27:23 - Execution 33:43 - Interpretation 36:57 - Q&A Session 44:59 - Proteintech Products Enjoyed the video? Make sure to hit that like button to show your support! Don't forget to subscribe to the channel for more awesome content, and share this video with your friends. Your support helps us keep creating—thanks for watching!