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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 7, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Greg Finak1, Juan-Manuel Perez, Andrew Weng

  • 1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fariview Ave N, Seattle, WA 98109, USA. gfinak@fhcrc.org

BMC Bioinformatics
|November 6, 2010
PubMed
Summary
This summary is machine-generated.

Optimizing data transformations in flow cytometry improves automated gating accuracy and reduces variability. Parameter-optimized biexponential and generalized Box-Cox transformations are recommended for fluorescence channels, enhancing high-throughput analysis.

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Area of Science:

  • * Computational Biology
  • * Data Science
  • * Immunology

Background:

  • * Flow cytometry data analysis requires robust preprocessing for high-throughput studies.
  • * Data transformation's impact on automated gating has been largely overlooked despite its influence on skewed distributions and varying intensities.
  • * Common transformations include generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox.

Purpose of the Study:

  • * To compare the performance of different data transformations for automated flow cytometry gating in a high-throughput setting.
  • * To optimize transformation parameters using maximum likelihood criteria.
  • * To evaluate the influence of parameter optimization on data visualization and gating accuracy.

Main Methods:

  • * Examined generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox transformations within the BioConductor flowCore framework.
  • * Developed maximum likelihood criteria to optimize transformation parameters based on data modeling assumptions.
  • * Compared parameter-optimized transformations against default parameters using real and simulated flow cytometry data.

Main Results:

  • * Parameter-optimized transformations significantly improved data visualization and reduced variability in cell population locations across samples.
  • * Optimized transformations decreased misclassification of individual events compared to default-parameter transformations.
  • * Recommended transformations varied by channel: biexponential or generalized Box-Cox for fluorescence, and optimized hyperbolic arcsine for scatter channels.

Conclusions:

  • * Parameter-optimized transformations enhance automated gating performance in high-throughput flow cytometry.
  • * Preferred transformations include biexponential or generalized Box-Cox for fluorescence and optimized hyperbolic arcsine for scatter channels.
  • * The developed algorithm is available in the publicly accessible BioConductor package, flowTrans.