Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
PLoS Comput Biol. 2019 Aug 30;15(8):e1007040. doi: 10.1371/journal.pcbi.1007040. eCollection 2019 Aug.
Single-cell RNA-sequencing (scRNA-seq) provides new opportunities to gain a mechanistic understanding of many biological processes. Current approaches for single cell clustering are often sensitive to the input parameters and have difficulty dealing with cell types with different densities. Here, we present Panoramic View (PanoView), an iterative method integrated with a novel density-based clustering, Ordering Local Maximum by Convex hull (OLMC), that uses a heuristic approach to estimate the required parameters based on the input data structures. In each iteration, PanoView will identify the most confident cell clusters and repeat the clustering with the remaining cells in a new PCA space. Without adjusting any parameter in PanoView, we demonstrated that PanoView was able to detect major and rare cell types simultaneously and outperformed other existing methods in both simulated datasets and published single-cell RNA-sequencing datasets. Finally, we conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations.
单细胞 RNA 测序 (scRNA-seq) 为深入了解许多生物学过程提供了新的机会。目前的单细胞聚类方法通常对输入参数敏感，并且难以处理密度不同的细胞类型。在这里，我们提出了全景视图 (PanoView)，这是一种迭代方法，集成了一种新颖的基于密度的聚类方法，即凸包排序局部最大值 (OLMC)，它使用启发式方法根据输入数据结构来估计所需的参数。在每次迭代中，PanoView 将识别最可信的细胞簇，并在新的 PCA 空间中用剩余的细胞重复聚类。在不调整 PanoView 中的任何参数的情况下，我们证明 PanoView 能够同时检测主要和罕见的细胞类型，并且在模拟数据集和已发表的单细胞 RNA-seq 数据集中均优于其他现有方法。最后，我们对胚胎小鼠下丘脑进行了 scRNA-Seq 分析，PanoView 能够揭示已知的细胞类型和几个罕见的细胞亚群。
