Cytobank Enterprise

Cytobank, yüksek boyutlu tek hücre verilerinin yapay zeka öğrenimi destekli analizini sunan bulut tabanlı bir platformdur. Kurumsal lisanslar özel bir buluta erişim sağlar ve kurumlarda veya biyofarma Ar-Ge veya klinik araştırma kuruluşlarında görev yapan daha kalabalık araştırma ekiplerinin ihtiyaçlarını karşılamak üzere tasarlanmıştır. İhtiyaçlarınıza göre farklı lisans türleri mevcuttur. Güçlü boyut azaltma, kümeleme ve tahmin algoritmaları araştırmanızı hızlandırır.  Akış ve kütle sitometresini veya diğer tek hücre verilerini yönetmek ve arşivlemek ve herhangi bir web tabanlı cihazdan farklı disiplinler ve coğrafyalar genelinde iş arkadaşlarınızla kolayca iş birliği yapmak için Cytobank platformunu kullanın.


Yalnızca Araştırma Amaçlı Kullanım İçindir. Tanılama prosedürlerinde kullanmak için uygun değildir.

Resources + Tools
Öğrenme Merkezi Ücretsiz Deneme Sürümü Teklif İste Destek
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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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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>
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.

Technical Documents

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