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

Bioinformatics
Z. ZouXuegong Zhang

Abstract

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 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 hierarchical clustering. 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. The R package of HGC is available at https://bioconductor.org/packages/HGC/. Supplementary data are available at Bioinformatics online.

References

May 19, 2018·Cell·Xiaoping HanGuoji Guo
Jun 21, 2019·Molecular Systems Biology·Malte D Luecken, Fabian J Theis
Aug 29, 2020·Nature Communications·Sarah Aldridge, Sarah A Teichmann

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