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.
Researchers at Johns Hopkins University School of Medicine have developed 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, the researchers 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, they conducted scRNA-Seq analysis of embryonic mouse hypothalamus, and PanoView was able to reveal known cell types and several rare cell subpopulations.
Panoramic view algorithm
(A) The schematic illustration of PanoView algorithm. (B-D) A toy model for the illustration of OLMC algorithm. (B) 500 random points in 2D space. Gray numbers represent the number of neighbors for each point. Colored numbers are three local maximum densities. (C) The histograms represent the distance to local maximums. The heights of colored bars are used for constructing the first convex hull for each local maximum. (D) Color-enclosed circles represent the convex hulls constructed by colored bars in (C) during OLMC algorithm.
Availability – Software files are available from a github repository (https://github.com/mhu10/scPanoView)