Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.
Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data.
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DOI：
10.1016/j.gpb.2020.09.006
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Chen JY
中科院分区：
文献类型：
--
作者：
Nguyen TM;Jeevan JJ;Xu N;Chen JY
在这项工作中，我们描述了极基尼曲线的发展，这是一种通过分析单细胞RNA测序（scRNA-seq）数据来表征聚类标记的方法。Polar Gini Curve结合基因表达和2D坐标（“空间”）信息，从scRNA-seq数据中检测任何聚类细胞中的均匀性模式。我们证明了Polar Gini Curve可以帮助用户描述特定簇中细胞的形状和密度分布，这可以在常规scRNA-seq数据分析过程中生成。为了量化基因在细胞簇空间中均匀分布的程度，我们将联合收割机两条极基尼曲线（PGC）--一条绘制在表达基因的细胞点上（“前景曲线”），另一条绘制在簇中的所有细胞点上（“背景曲线”）。我们发现，具有高度不同的前景和背景曲线的基因往往不会均匀分布在细胞簇中，从而在簇内具有空间上不同的基因表达模式。具有相似前景和背景曲线的基因倾向于均匀地分布在细胞簇中，因此在簇内具有均匀的基因表达模式。PGC的这种定量属性可以应用于从scRNA-seq数据灵敏地发现跨簇的生物标志物。我们在几个模拟案例研究中展示了极基尼曲线框架的性能。使用这个框架来分析真实世界的新生小鼠心脏细胞数据集，检测到的生物标志物可以表征心肌细胞的新亚型。Polar Gini Curve的源代码和数据可以在http://discovery.informatics.uab.edu/PGC/或https://figshare.com/projects/Polar_Gini_Curve/76749上找到。
In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates (“spatial”) information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)—one drawn upon the cell-points expressing the gene (the “foreground curve”) and the other drawn upon all cell-points in the cluster (the “background curve”). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster—thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster—thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.
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发表时间：
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影响因子：
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