Automatic phenotyping using exhaustive projection pursuit

One of the most common objectives in the analysis of flow cytometry data is the identification and delineation of phenotypes, distinct populations of cells with shared characteristics in the measurement dimensions. We have developed an automated tool to comprehensively identify these cell populations by Exhaustive Projection Pursuit (EPP). The method evaluates all two-dimensional projections among the suitable data dimensions and creates an optimized sequence of statistically significant gating regions that identify all phenotypes supported by the data. We evaluate the results of EPP on four well characterized data sets from the literature. The C++ code for EPP can be called from any computing environment. We illustrate this with a MATLAB utility that integrates EPP with FlowJo. All source code is freely available.

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Publication Details

Journal
Communications Biology
Published
2025-08-12
DOI
https://doi.org/10.1038/s42003-025-08581-z
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Automatic phenotyping using exhaustive projection pursuit

Connor Meehan, Guenther Walther, Stephen Meehan, Leonore A. Herzenberg et al.
Communications Biology
Image Processing Techniques and Applications
article

Automatic phenotyping using exhaustive projection pursuit

Connor Meehan, Guenther Walther, Stephen Meehan, Leonore A. Herzenberg, Wayne Moore, David R. Parks
article en

Abstract

One of the most common objectives in the analysis of flow cytometry data is the identification and delineation of phenotypes, distinct populations of cells with shared characteristics in the measurement dimensions. We have developed an automated tool to comprehensively identify these cell populations by Exhaustive Projection Pursuit (EPP). The method evaluates all two-dimensional projections among the suitable data dimensions and creates an optimized sequence of statistically significant gating regions that identify all phenotypes supported by the data. We evaluate the results of EPP on four well characterized data sets from the literature. The C++ code for EPP can be called from any computing environment. We illustrate this with a MATLAB utility that integrates EPP with FlowJo. All source code is freely available.

Communications BiologyVol. 8(1)
Stanford Medicine (US), Stanford University (US)
National Science Foundation, National Institutes of Health, Genentech
Openalex Percentile: Top 72%
Image Processing Techniques and Applications
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