Cytobank enterprise

Cytobank è una piattaforma cloud che esegue analisi di dati high dimensional su singole cellule tramite machine-learning. Le licenze forniscono l'accesso a un cloud privato e sono progettate per soddisfare le esigenze di grandi team di ricerca in istituti, reparti R&D di industrie biofarmaceutiche o organizzazioni per la ricerca clinica. Sono disponibili diversi tipi di licenza a seconda delle vostre esigenze. La piattaforma dispone di potenti algoritmi di riduzione della dimensionalità, clustering e predizione per accelerare la vostra ricerca.  Con la piattaforma Cytobank potrete gestire e archiviare i dati di citofluorimetria e citometria di massa o altri dati su singole cellule per collaborare con i colleghi di diverse discipline e aree geografiche da qualsiasi dispositivo provvisto di connessione ad internet.


Solo per scopi di ricerca. Non è destinato all'utilizzo in procedure diagnostiche.

Resources + Tools
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Cytobank Enterprise Features

Reduce Subjectivity

Data to Insight

Collaborate

Democratize ML-assisted Analysis

  • Made for biologists, no coding or plugins required, visit the Learning Center
  • Online knowledge repository full of articles, tips and tricks and support request form for new questions
  • Use the Experiment Manager to organize and find your projects, use the tree view to show relationships between experiments

Explore Cytobank Enterprise Licenses

Explore Cytobank Enterprise Models

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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Utilizzo di algoritmi di apprendimento automatico per informazioni approfondite sulla composizione dei sottogruppi cellulari
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>
Get the Cytobank Platform and Publish
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.

Documenti tecnici

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For Research Use Only. Not for use in diagnostic procedures.