PanoView: An iterative clustering method for single-cell RNA sequencing data

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（Single-cell RNA-sequencing, scRNA-seq）为解析诸多生物过程的机制提供了全新契机。当前常用的单细胞聚类方法往往对输入参数较为敏感，且难以适配不同密度的细胞类型。本研究提出全景视图（Panoramic View, PanoView）——一种集成了新型基于密度聚类算法的迭代方法，该算法名为基于凸包的局部极大值排序（Ordering Local Maximum by Convex hull, OLMC），并采用启发式策略根据输入数据的结构估算所需参数。在每次迭代中，PanoView会先识别置信度最高的细胞簇，随后针对剩余细胞在新的主成分分析（Principal Component Analysis, PCA）空间中重复聚类流程。无需调整PanoView的任何参数，本研究验证表明PanoView可同时识别主要细胞类型与稀有细胞类型，且在模拟数据集及已发表的单细胞RNA测序数据集上均优于现有其他方法。最后，本研究对小鼠胚胎下丘脑开展了scRNA-seq分析，结果显示PanoView可成功识别已知细胞类型以及多种稀有细胞亚群。