SPADEVizR: an R package for visualization, analysis and integration of SPADE results.
SPADEVizR: an R package for visualization, analysis and integration of SPADE results.
复制标题
DOI：
10.1093/bioinformatics/btw708
复制
发表时间：
2017-03-01
期刊：
影响因子：
--
通讯作者：
Tchitchek N
中科院分区：
文献类型：
--
作者：
Gautreau G;Pejoski D;Le Grand R;Cosma A;Beignon AS;Tchitchek N
流式、高光谱和质谱细胞术是在单细胞水平上测量细胞标志物表达的实验技术。最近，可同时测量的标记物数量的增加导致了新的自动门控算法的发展。特别是，SPADE算法已被提出作为一种新的方式来识别具有相似表型的细胞在高维细胞计数数据的集群。虽然SPADE或其他细胞聚类算法是强大的方法，但需要补充分析功能以更好地表征所识别的细胞簇。我们已经开发了SPADEVizR，一个R软件包，用于可视化，分析和整合细胞聚类结果。可用的统计方法允许突出显示具有相关生物学行为的细胞群或将它们与其他生物学变量整合。此外，有几种可视化方法可用于更好地表征细胞簇，例如火山图，流图，平行坐标，热图或distogram。SPADEVizR还可以基于细胞簇丰度生成线性、考克斯或随机森林模型来预测生物学结果。此外，SPADEVizR具有几个功能，可以量化和可视化细胞聚类结果的质量。这些分析特征对于更好地解释鉴定的艾德细胞簇的行为和表型至关重要。重要的是，SPADEVizR可以处理来自SPADE以外的其他算法的聚类结果。SPADEVizR在GPL-3许可下发布，可在https://github.com/tchitchek-lab/SPADEVizR上获得。 补充数据可在Bioinformatics在线获得。
Flow, hyperspectral and mass cytometry are experimental techniques measuring cell marker expressions at the single cell level. The recent increase of the number of markers simultaneously measurable has led to the development of new automatic gating algorithms. Especially, the SPADE algorithm has been proposed as a novel way to identify clusters of cells having similar phenotypes in high-dimensional cytometry data. While SPADE or other cell clustering algorithms are powerful approaches, complementary analysis features are needed to better characterize the identified cell clusters. We have developed SPADEVizR, an R package designed for the visualization, analysis and integration of cell clustering results. The available statistical methods allow highlighting cell clusters with relevant biological behaviors or integrating them with additional biological variables. Moreover, several visualization methods are available to better characterize the cell clusters, such as volcano plots, streamgraphs, parallel coordinates, heatmaps, or distograms. SPADEVizR can also generate linear, Cox or random forest models to predict biological outcomes, based on the cell cluster abundances. Additionally, SPADEVizR has several features allowing to quantify and to visualize the quality of the cell clustering results. These analysis features are essential to better interpret the behaviors and phenotypes of the identified cell clusters. Importantly, SPADEVizR can handle clustering results from other algorithms than SPADE. SPADEVizR is distributed under the GPL-3 license and is available at https://github.com/tchitchek-lab/SPADEVizR. Supplementary data are available at Bioinformatics online.