使用 Python 中的 FlowSOM 进行高效的细胞计数分析可提高与其他单细胞工具的互操作性。
Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools.
机构信息
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium.
出版信息
Bioinformatics. 2024 Mar 29;40(4). doi: 10.1093/bioinformatics/btae179.
MOTIVATION
We describe a new Python implementation of FlowSOM, a clustering method for cytometry data.
RESULTS
This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot.
AVAILABILITY AND IMPLEMENTATION
The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.
动机
我们描述了一种新的 Python 实现的 FlowSOM，这是一种用于细胞仪数据的聚类方法。
结果
这个实现比在 R 中的原始版本更快，更适合与单细胞组学数据一起使用，包括与当前的单细胞数据结构的集成，并包含所有原始的可视化，如星图和饼图。
可用性和实现
FlowSOM 的 Python 实现可在 GitHub 上免费获得：https://github.com/saeyslab/FlowSOM_Python。
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