February 23, 2017  |  Education, Flow Cytometry, viSNE  |  By  |  0 Comments

Five Key Takeaways from Our Latest viSNE Webinar

We recently hosted a very popular webinar, “Placing viSNE in Your Toolbox” featuring special guest Dr. Anna Belkina of Boston University School of Medicine. More than 500 investigators registered for our event to learn about cutting-edge tools and techniques for optimizing results from high-dimensional cytometry datasets.

Cytobank screenshots from Bendall et.al. Science 2011
Cytobank screenshots on Bendall et.al. Science 2011 data

Missed our latest viSNE presentation?
View the webinar recording here:

Overview:

High-dimensional flow cytometry data (both fluorescent or mass) require new visualization and analysis tools such as viSNE/t-SNE. Cytobank is equipped to handle these high parameter data and transform it quickly into results.

In last week’s webinar, we explored and demonstrated how to leverage advanced viSNE techniques (more specifically, Cytobank’s advanced viSNE tools) to automate and optimize analysis of large cytometry datasets with precision. Read on to see our top five takeaways from the webinar.

Five key points from Dr. Anna Belkina’s presentation,
“Placing viSNE in Your Toolbox”:

  1. viSNE is a dimensionality-reduction algorithm that works well for flow cytometry data, as well as many other data types.  Cytobank will soon accept direct uploads of many more new data types. Keep an eye out for an invitation in Cytobank to beta test as soon as this new feature goes live.
  2. Analyze many cell events to detect rare populations:
Source: Hoy T, DAKO Guide to Flow Cytometry, 2006
Source: Hoy T, DAKO Guide to Flow Cytometry, 2006


  • viSNE parameters require adjustment to resolve major and minor cell populations in large datasets.

    Source: Belkina, AC. Unpublished data.
  • Cytobank’s viSNE implementation provides the necessary flexibility to define and optimize viSNE parameters, such as iterations and perplexity.
    viSNEsettings
  • Combining data analysis tools, for example viSNE+SPADE, creates an analysis pipeline aimed at extracting maximum information from high parameter data.

 

viSNE on SPADE clustered population overlay in Cytobank
viSNE map displaying SPADE clustered population overlay in Cytobank

 

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