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Ultrafast clustering of single-cell flow cytometry data using FlowGrid

Xiaoxin Ye, Joshua W K Ho
doi: https://doi.org/10.1101/394189
Xiaoxin Ye
Victor Chang Cardiac Research Institute
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Joshua W K Ho
Victor Chang Cardiac Research Institute
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  • For correspondence: j.ho@victorchang.edu.au
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Abstract

Flow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells. Here, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid can complete clustering on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or fail to produce any clustering result. FlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 17, 2018.
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Ultrafast clustering of single-cell flow cytometry data using FlowGrid
Xiaoxin Ye, Joshua W K Ho
bioRxiv 394189; doi: https://doi.org/10.1101/394189
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Ultrafast clustering of single-cell flow cytometry data using FlowGrid
Xiaoxin Ye, Joshua W K Ho
bioRxiv 394189; doi: https://doi.org/10.1101/394189

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