FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

被引：1282

作者：

Van Gassen, Sofie ^{[1
,2
,3
]}

Callebaut, Britt ^{[1
]}

Van Helden, Mary J. ^{[2
,3
]}

Lambrecht, Bart N. ^{[2
,3
]}

Demeester, Piet ^{[1
]}

Dhaene, Tom ^{[1
]}

Saeys, Yvan ^{[2
,3
]}

机构：

[1] Univ Ghent, Dept Informat Technol, IMinds, B-9000 Ghent, Belgium

[2] VIB, Inflammat Res Ctr, Ghent, Belgium

[3] Ghent Univ Hosp, Dept Resp Med, Ghent, Belgium

来源：

CYTOMETRY PART A | 2015年 / 87A卷 / 07期

关键词：

polychromatic flow cytometry; mass cytometry; exploratory data analysis; visualization method; self-organizing map; bioinformatics; IDENTIFICATION; ALGORITHM;

D O I：

10.1002/cyto.a.22625

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at and will be made available at Bioconductor. (c) 2015 International Society for Advancement of Cytometry

引用

页码：636 / 645

页数：10

共 18 条

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[2]

Rapid Cell Population Identification in Flow Cytometry Data [J].