High Parameter Discovery Employing Dimensionality Reduction and Clustering 5/16/19

Technological advances have allowed for an increasing number of parameters to be simultaneously assessed by cytometric methods. With this trend towards higher parameter data, correct identification of cells using a traditional manual gating approach has become increasingly cumbersome and problematic. Fortunatly, a number of automated dimensionality reduction and clustering tools have been developed that can aid in the analysis of high parameter data to identify complex populations that may be overlooked or difficult to find using manual gating. This Webinar will give an overview of the available dimensionality reduction and clustering tools available in FlowJo v10, and walk users though a workflow (Cleanup→DownSample→ Concatenate→Dimensionally Reduce→Cluster) that may be employed to identify complex phenotypic populations and determine how those populations change over experimental conditions.

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