12. A New Paradigm for Cytometric Analysis

  1. Kandice Kottke-Marchant MD, PhD2,3,4 and
  2. Bruce H. Davis MD5
  1. C. Bruce Bagwell MD, PhD

Published Online: 8 AUG 2012

DOI: 10.1002/9781444398595.ch12

Laboratory Hematology Practice

Laboratory Hematology Practice

How to Cite

Bagwell, C. B. (2012) A New Paradigm for Cytometric Analysis, in Laboratory Hematology Practice (eds K. Kottke-Marchant and B. H. Davis), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9781444398595.ch12

Editor Information

  1. 2

    Pathology & Laboratory Medicine Institute, Cleveland, OH, USA

  2. 3

    Department of Pathology, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA

  3. 4

    Hemostasis and Thrombosis, Department of Clinical Pathology, Cleveland Clinic, Cleveland, OH, USA

  4. 5

    Trillium Diagnostics, LLC, Bangor, ME, USA

Author Information

  1. Verity Software House, Topsham, ME, USA

Publication History

  1. Published Online: 8 AUG 2012
  2. Published Print: 10 APR 2012

ISBN Information

Print ISBN: 9781405162180

Online ISBN: 9781444398595

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Keywords:

  • flow cytometry;
  • multidimensional analysis;
  • dimensionality barrier;
  • hematopoietic differentiation;
  • probability state model

Summary

Cytometry is likely to be the technology of choice for observing and understanding cellular processes in the foreseeable future. Unfortunately, there are problems with the current flow cytometry technology that limit its full potential. Some of these limitations include parameter scalability, hierarchical gating errors, and multiple sample analysis integration and visualization.

A new paradigm has been proposed that is based on a probability state model capable of representing the rich multiparameter information embedded in cytometric listmode files. Model-derived parametric plots merge intensity, and percentage, as well as variance information into easy-to-understand graphical formats that represent virtually every bit of information contained in these files. Normal bone marrow specimens analyzed with this new approach reveal lineage relationships not easily appreciated with conventional analysis approaches and may change our understanding of what “normal” really means.

The major advantage of the probability state modeling system is that it scales well with number of parameters or measurement, so largely eliminates the dimensionality barrier for data analysts to extract information. Because this system stochastically classifies events, it also appropriately accounts for population overlap and thus minimizes the need for the usual cytometric gates and analysis regions. Multiple listmode files can be integrated into a single model if there are appropriate common parameters. Finally, the system presents high dimensional cytometry data in an understandable format that needs little interpretation.