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bioRxiv · 10.1101/765628

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

stassen, s. V.·Siu, D. M. D.·Lee, K. C. M.·Ho, J. W. K.·So, H. K. H.·Tsia, K.
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Abstract

MotivationNew 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.\n\nResultsWe introduce a highly scalable graph-based clustering algorithm PARC - phenotyping by accelerated refined community-partitioning - for ultralarge-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell 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 to the next fastest graph-clustering algorithm, Phenograph. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.\n\nAvailability and Implementationhttps://github.com/ShobiStassen/PARC

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stassen, s. V., Siu, D. M. D., Lee, K. C. M., Ho, J. W. K., So, H. K. H., Tsia, K.. 2019-09-11. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells. https://doi.org/10.1101/765628

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