PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

Abstract

MOTIVATION: New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.

RESULTS: We introduce a highly scalable graph-based clustering algorithm PARC - Phenotyping by Accelerated Refined Community-partitioning - for large-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-sampling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to > 2 hours for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.

AVAILABILITY: https://github.com/ShobiStassen/PARC.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

© The Author(s) 2020. Published by Oxford University Press.