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Related Experiment Videos

Pattern sorting: a computer-controlled multidimensional sorting method using k-d trees

M Bigos1, D R Parks, W A Moore

  • 1Department of Genetics, Stanford University School of Medicine, California 94305-5125.

Cytometry
|August 1, 1994
PubMed
Summary
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Multidimensional binary trees offer efficient real-time sorting for flow cytometry. This method enables complex N-dimensional data analysis beyond simple two-dimensional projections, improving sorting capabilities.

Area of Science:

  • Biotechnology
  • Computational Biology
  • Data Science

Background:

  • Traditional flow cytometry sorting relies on lookup tables, limiting analysis to two-dimensional projections.
  • Complex multidimensional data analysis, such as principal components or clustering, is challenging with conventional methods.

Purpose of the Study:

  • To introduce multidimensional binary trees as a memory-efficient and general method for real-time flow cytometry sorting.
  • To demonstrate the capability of this approach for handling N-dimensional data spaces that cannot be represented by 2D projections.

Main Methods:

  • Implementation of a prototype system utilizing multidimensional binary trees for sorting decisions.
  • Description of the fundamental advantages over conventional lookup table sorting techniques.

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Main Results:

  • Multidimensional binary trees effectively implement sort criteria in the full N-dimensional data space.
  • The method is particularly relevant for advanced multidimensional analysis techniques.

Conclusions:

  • Multidimensional binary trees offer a superior approach for real-time sorting in flow cytometry.
  • This method enhances the ability to perform complex analyses on multidimensional datasets, with potential for further implementation.