HGC: fast hierarchical clustering for large-scale single-cell data

• DOI: 10.1093/bioinformatics/btab420
• Publication Date: 2021-06-04T11:44:02Z
• Authors: Ziheng Zou; Kui Hua; Xuegong Zhang
• Keywords: Hierarchical clustering

Abstract Summary Clustering is a key step in revealing heterogeneities single-cell data. Most existing clustering methods output fixed number of clusters without the hierarchical information. Classical (HC) provides dendrograms cells, but cannot scale to large datasets due high computational complexity. We present HGC, fast Hierarchical Graph-based tool address both problems. It combines advantages graph-based and HC. On shared nearest-neighbor graph HGC constructs tree with linear time Experiments showed that enables multiresolution exploration biological hierarchy underlying data, achieves state-of-the-art accuracy on benchmark data can datasets. Availability implementation The R package available at https://bioconductor.org/packages/HGC/. Supplementary information are Bioinformatics online.