A comparison framework and guideline of clustering methods for mass cytometry data
A comparison framework and guideline of clustering methods for mass cytometry data
复制标题
质谱流式数据聚类方法的比较框架和指南
DOI：
10.1186/s13059-019-1917-7
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发表时间：
2019-12-23
期刊：
影响因子：
12.3
通讯作者：
Ding, Xianting
中科院分区：
文献类型：
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作者：
Liu, Xiao;Song, Weichen;Ding, Xianting
随着质量细胞术在医学研究中的应用不断扩大，各种各样的聚类方法，包括半监督和无监督的聚类方法，已经被开发出来用于数据分析。选择最优聚类方法可以加快有意义细胞群体的识别。为了解决这一问题，我们基于6个独立的基准数据集，比较了9种方法的3类性能指标，即“精度”作为外部评价，“一致性”作为内部评价和稳定性。七种无监督方法（Accense, Xshift, PhenoGraph, FlowSOM, flowMeans， DEPECHE和kmeans）和两种半监督方法（自动细胞类型发现和分类和线性判别分析（LDA））在六个细胞计数数据集上进行了测试。我们针对随机子抽样、不同的样本量和每种方法的群集数量计算和比较所有定义的性能度量。LDA最精确地复制了手动标签，但在内部评估中排名不高。与其他无监督工具相比，PhenoGraph和FlowSOM在精度、一致性和稳定性方面表现更好。在检测精细的子簇时，PhenoGraph和Xshift的鲁棒性更强，而DEPECHE和FlowSOM倾向于将类似的簇分组为元簇。随着样本量的增加，PhenoGraph、Xshift和flowMeans的性能会受到影响，但FlowSOM随着样本量的增加相对稳定。结论在选择合适的细胞仪数据分析工具时，应综合考虑精密度、一致性、稳定性和聚类分辨率。因此，我们根据这些特征为一般读者提供决策指南，以便更容易地选择最合适的聚类工具。
BackgroundWith the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.ResultTo address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.ConclusionAll the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.