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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 16, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Tree-Based Methods for Discovery of Association between Flow Cytometry Data and Clinical Endpoints.

M Eliot1, L Azzoni, C Firnhaber

  • 1Division of Biostatistics, University of Massachusetts, Amherst, MA 01003, USA.

Advances in Bioinformatics
|February 11, 2010
PubMed
Summary
This summary is machine-generated.

Tree-based algorithms like CART, random forests, and logic regression effectively analyze flow cytometry data. These methods reveal combinations of immune markers that predict CD4 T-cell recovery in HIV-1 patients on antiretroviral therapy.

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Area of Science:

  • Immunology
  • Computational Biology
  • Biostatistics

Background:

  • Flow cytometry is crucial for monitoring immune status in HIV-1 patients.
  • CD4 T-cell recovery is a key indicator of antiretroviral therapy (ART) effectiveness.
  • Predicting CD4 T-cell recovery aids in personalized HIV treatment strategies.

Purpose of the Study:

  • To apply and compare three tree-based algorithms (CART, RFs, LR) for analyzing flow cytometry data.
  • To identify key predictors of CD4 T-cell recovery in HIV-1 infected individuals with baseline CD4 counts between 200-350 cells/μL.
  • To compare tree-based methods with traditional contingency table analysis.

Main Methods:

  • Application of Classification and Regression Trees (CART), Random Forests (RFs), and Logic Regression (LR) to flow cytometry data.
  • Analysis focused on predicting CD4 T-cell count recovery post-ART initiation.
  • Comparison of findings with standard contingency table analysis.

Main Results:

  • Tree-based methods, particularly CART and LR, identified combinations of immune markers predictive of CD4 T-cell recovery.
  • Baseline CD3-DR-CD56+CD16+ was consistently identified as an important predictor.
  • Immune activation states, especially CD8 T-cell activation, emerged as strong predictors when analyzed in combination.

Conclusions:

  • Tree-based algorithms offer advanced analytical capabilities for complex flow cytometry datasets.
  • These methods can uncover novel associations and combinations of immune markers not apparent through univariate analysis.
  • Tree-based approaches enhance the understanding of factors influencing CD4 T-cell recovery in HIV-1 patients.