Automatic Gating

Manual gating is often cited as a major contributor to variability in cytometry assays. And we all know how time consuming it can be. With Automatic gating on the Cytobank platform we are adding the power of machine learning-assisted analysis to hypothesis-driven experiments, where you know what you are looking for, but you need help to scale up your data analysis.

Cytobank Automatic gating allows you to define a set of populations to be identified and the criteria for how they should be gated, faithfully replicating the manual analysis you would perform. The model can be trained for your own panel using your gating strategy on a small number of samples and applied to hundreds of files. Automatic gating can reduce the time needed for, and variability introduced by, manual gating.

In these videos, Dr. Nicole Weit – technical product manager here at Beckman Coulter Life Sciences – shows you:

How Automatic gating can improve your hypothesis-driven analysis


How to train a new Automatic gating model on the Cytobank platform


How to use a trained model to automatically analyze new data


How to evaluate the performance of an Automatic gating model and how to use the results


Cytobank Automatic Gating Resources

Cytobank v10 Automatic Gating Application Note
Application Note
How to establish and evaluate machine learning-assisted automatic gating to improve reproducibility and reduce time spent on your flow cytometry data analysis.

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Cytobank v10 Automatic Gating Whitepaper
Hypothesis-driven analysis of a 19-color deep immunophenotyping panel using automatic gating.

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Cytobank software is a cloud-based platform for the analysis, storage, and sharing of flow and mass cytometry data. It offers machine learning-assisted analysis of high-dimensional, single-cell data and is designed to let you easily collaborate with colleagues from different departments and regions from any web-enabled device.

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