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

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

Automated summary

HGC is a fast Hierarchical Graph-based Clustering tool that addresses the issues of fixed number of clusters and lack of hierarchical information in single-cell data clustering. Experiments demonstrate that HGC enables multiresolution exploration of biological hierarchy, achieves state-of-the-art accuracy on benchmark data, and is capable of scaling to large datasets.