Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.
Cytometry A. 2011 Jan;79(1):6-13. doi: 10.1002/cyto.a.21007.
We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.
我们开发了 flowMeans，这是一种基于 K-均值聚类的高效、准确的流式细胞术 (FCM) 数据中自动识别细胞群体的方法。与传统的 K-均值聚类不同，flowMeans 可以通过使用多个聚类来模拟单个群体来识别凹形细胞群体。flowMeans 使用一个变化点检测算法来确定亚群的数量，从而使该方法能够用于高通量 FCM 数据分析管道。我们的方法与人类专家的手动分析和当前最先进的自动化门控算法相比具有优势。flowMeans 作为一个开放源代码的 R 包通过 Bioconductor 免费提供。
