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Related Concept Videos

Arrhenius Plots02:34

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The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
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Related Experiment Video

Updated: Jan 30, 2026

Quantification of Proliferating Human Antigen-specific CD4+ T Cells using Carboxyfluorescein Succinimidyl Ester
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Penalized Supervised Star Plots: Example Application in Influenza-Specific CD4+ T Cells.

Tyson H Holmes1,2, Priyanka B Subrahmanyam2,3, Weiqi Wang2,3

  • 11 Department of Medicine, Stanford University School of Medicine, Stanford, California.

Viral Immunology
|January 31, 2019
PubMed
Summary

We developed penalized supervised star plots, a new visualization tool to effectively identify and report novel immune cell phenotypes from complex high-dimensional data. This method preserves crucial phenotypic information for better biological insights.

Keywords:
penaltyflow cytometryimmune cell phenotypesinfluenzamass cytometryregularization

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Area of Science:

  • Immunology
  • Computational Biology
  • Data Visualization

Background:

  • Immune cell phenotypes are defined by high-dimensional marker signatures.
  • Discovering new phenotypes requires analyzing complex flow and mass cytometry data.
  • Effective reporting necessitates low-dimensional visualization that preserves high-dimensional structure.

Purpose of the Study:

  • Introduce penalized supervised star plots for visualizing immune cell phenotypes.
  • Develop a method to preserve high-dimensional phenotypic structure in 2D projections.
  • Ensure robustness and portability of the visualization technique.

Main Methods:

  • Utilized cluster analysis on high-throughput cytometry data.
  • Developed penalized supervised star plots as a 2D projection technique.
  • Incorporated cross-validation for projection portability.

Main Results:

  • Penalized supervised star plots preserve cluster separation and marker contribution information.
  • The method demonstrates robustness against non-informative markers.
  • Comparative analysis showed advantages over existing visualization procedures.
  • Successfully applied to influenza-specific T cell and peripheral blood mononuclear cell data.

Conclusions:

  • Penalized supervised star plots offer a powerful tool for discovering and reporting novel immune cell phenotypes.
  • The method enhances the interpretability of high-dimensional cytometry data.
  • Cross-validation ensures reliable application across different sample sets.