Cytobank Enterprise

The cloud-based Cytobank data analysis platform is an ideal solution for immunologists and cytometrists who need to quickly gain insights from large datasets while having confidence in their results, reducing subjectivity of their analysis and eliminating error-prone and time-consuming steps. It offers fully integrated and quality-tested tools for advanced cytometry data analysis and statistical evaluation, providing comprehensive workflow coverage and automation.

Enterprise licenses provide access to a private cloud and are designed to meet the needs of larger research teams in institutions, biopharma R&D or clinical research organizations. Different license types are available depending on your needs.

We offer powerful algorithms for automatic data clean up, dimensionality reduction, automated gating, clustering and biomarkers prediction to accelerate your research. Use the Cytobank platform to manage and archive flow and mass cytometry or other single-cell data and to easily collaborate with colleagues across disciplines and geographies from any web-based device.

  
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Licenses

Cytobank Enterprise Specifications

Compatibility FCS 2.0, 3.0, and 3.1 files from instrument agnostic. DROP is able to import any numeric data in a text delimited (comma, semicolon, tab) format.
Plot Types Contour Plots, Density Plots, Dot Plots, Heatmap, Histograms, Overlay Plots
Algorithms CITRUS, FlowSOM, SPADE, viSNE, UMAP, opt-SNE, tSNE-CUDA, Automatic Gating, PeacoQC
Statistics
  • Student’s t-test
  • Mann-Whitney U test
  • Paired student’s t-test
  • Wilcoxon signed-rank test
  • Kruskal-Wallis H test
  • One-way analysis of variance
  • Two-way analysis of variance
License Type Academic, Commercial
License Term 1–3 Years

Content and Resources

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Using Standardized Dry Antibody Panels for Flow Cytometry in Response to SARS-CoV2 Infection As a highly standardized reagent set for comprehensive immune profiling, dry DURAClone* antibody panels (Beckman Coulter) were extended by adding antibodies in liquid format and evaluated for their utility as straightaway immune profiling research tools in normal and SARS-CoV2-positive donors.
Using Machine Learning Algorithms to Provide Deep Insights into Cellular Subset Composition
Use Machine Learning Algorithms to Explore the Potential of Your High Dimensional Flow Cytometry Data Example of a 20–color Panel on CytoFLEX LX Explore the potential of high dimensional flow cytometry data with an Example of a 20–color Panel on CytoFLEX LX. Understand how to perform machine learning algorithms like viSNE and FlowSOM to identify phenotypes of populations/subsets present in the 20–color CytoFLEX LX flow cytometry data. Build a computational flow cytometry data analysis pipeline with Cytobank. Learn how to assess the quality of viSNE maps and FlowSOM clustering results. Recognize how pre–processing steps can affect the result quality of machine learning algorithms.
How to use R to rewrite FCS files with different number of channels <span style="color: #183247; background-color: #ffffff;">How to use R to rewrite FCS files with different number of channels</span>
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For Research Use Only. Not for use in diagnostic procedures.