Subpopulation Detection and Their Comparative Analysis across Single-Cell Experiments with scPopCorn.
Subpopulation Detection and Their Comparative Analysis across Single-Cell Experiments with scPopCorn.
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DOI：
10.1016/j.cels.2019.05.007
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发表时间：
2019-06
期刊：
影响因子：
9.3
通讯作者：
Yijie Wang;Jan Hoinka;T. Przytycka
中科院分区：
文献类型：
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作者：
Yijie Wang;Jan Hoinka;T. Przytycka
单细胞实验中细胞亚群的鉴定以及实验间此类亚群的比较是最常进行的单细胞数据分析。这个重要的任务仍然等待一个完全令人满意的计算解决方案。为了满足这一需求，我们引入了一种计算方法，单细胞亚群比较（scPopCorn）。利用来自所有输入数据集的信息，scPopCorn通过优化联合目标函数同时执行这两项任务。优化涉及细胞群体的凝聚力的测量，结合Google的个性化PageRank方法，指导亚群检测，而细胞间相似性的测量用于指导映射。scPopCorn不仅优于目前使用的方法，而且还引入了数学概念，可以作为改进其他工具的垫脚石。
The identification of subpopulations of cells in single-cell experiments, and the comparison of such subpopulations across experiments are among the most frequently performed analysis of single-cell data. This important task still awaits a fully satisfying computational solution. To address this need, we introduce a computational method, single-cell subpopulations comparison (scPopCorn). Leveraging the information from all input datasets, scPopCorn performs these two tasks simultaneously by optimizing a joint objective function. The optimization involves a measure of cohesiveness of a cell population, which combined with Google's personalized PageRank approach, guides subpopulation detection, while a measure of cell-to-cell similarity is used to guide the mapping. scPopCorn not only outperforms currently used approaches but also introduces mathematical concepts that can serve as stepping stones to improve other tools.