使用穷举投影寻踪的自动表型分析

Automatic phenotyping using exhaustive projection pursuit.

作者信息

Moore Wayne A, Meehan Stephen W, Meehan Connor, Parks David R, Walther Guenther, Herzenberg Leonore A

机构信息

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

Independent Scholar, British Columbia, Canada.

出版信息

Commun Biol. 2025 Aug 12;8(1):1207. doi: 10.1038/s42003-025-08581-z.

DOI:10.1038/s42003-025-08581-z

PMID:40797028

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12343891/

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.

摘要

流式细胞术数据分析中最常见的目标之一是识别和描绘表型，即在测量维度上具有共同特征的不同细胞群体。我们开发了一种自动化工具，通过穷举投影寻优法（EPP）全面识别这些细胞群体。该方法评估合适数据维度中的所有二维投影，并创建一个优化的具有统计学意义的门控区域序列，以识别数据支持的所有表型。我们在文献中四个特征明确的数据集上评估了EPP的结果。EPP的C++代码可从任何计算环境调用。我们用一个将EPP与FlowJo集成的MATLAB实用程序对此进行了说明。所有源代码均可免费获取。