Fast, Metadata-Driven Analysis and Visualization of Flow and Mass Cytometry Data with CellEngine, Upcoming Webinar Hosted by Xtalks


A large variety of metadata-driven visualizations can be created, including ones combining categorical and continuous study metadata with the cytometry data.

Common challenges faced with existing flow cytometry software include speed, ability to create complex visualizations of large studies and meeting regulatory compliance. CellEngine was built from the ground-up by CellCarta to solve these challenges.

Today, CellEngine has been used to analyze nearly 5 million Flow Cytometry Standard (FCS) files in record time by users around the world. Analysis pipelines containing FlowSOM (which analyzes flow or mass cytometry data using a Self-Organizing Map), UMAP (Uniform Manifold Approximation and Projection), PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding) can be run on millions of cells in seconds to minutes. A large variety of metadata-driven visualizations can be created, including ones combining categorical and continuous study metadata with the cytometry data.

As a cloud-based application, CellEngine makes it easy to collaborate with colleagues while keeping your data safe, secure and centrally organized. CellEngine is rigorously validated, has an industry-leading uptime service-level agreement (SLA) and has all of the features required for use in Title 21 of the US Code of Federal Regulations (CFA), Part 11 compliant environments.

Register for this webinar to learn how to achieve fast, complex visualizations of cytometry data and meet regulatory requirements.

Join Zachary Bjornson-Hooper, PhD, Sr. Director, Informatics, CellCarta; Dave McIlwain, PhD, Senior Research Scientist, Stanford University School of Medicine, Nolan Lab; and Pier Federico Gherardini, PhD, Computational Biology Consultant, Pragmatica.Bio, for the live webinar on Friday, February 4, 2022 at 4pm EST.

For more information, or to register for this event, visit Fast, Metadata-Driven Analysis and Visualization of Flow and Mass Cytometry Data with CellEngine.

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