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minicore: Fast scRNA-seq clustering with various distances

View ORCID ProfileDaniel N. Baker, Nathan Dyjack, Vladimir Braverman, View ORCID ProfileStephanie C. Hicks, View ORCID ProfileBen Langmead
doi: https://doi.org/10.1101/2021.03.24.436859
Daniel N. Baker
1Department of Computer Science, Johns Hopkins University
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  • For correspondence: dbaker49@jhu.edu langmea@cs.jhu.edu
Nathan Dyjack
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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Vladimir Braverman
1Department of Computer Science, Johns Hopkins University
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Stephanie C. Hicks
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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Ben Langmead
1Department of Computer Science, Johns Hopkins University
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  • For correspondence: dbaker49@jhu.edu langmea@cs.jhu.edu
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Abstract

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicore’s novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattacharyya distance, which can be directly applied to count data and probability distributions.

Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (<2-fold) speed differences among all distances. We show that a minicore pipeline consisting of k-means++, localsearch++ and minibatch k-means can cluster a 4-million cell dataset in minutes, using less than 10GiB of RAM. This memory-efficiency enables atlas-scale clustering on laptops and other commodity hardware. Finally, we report findings on which distance measures give clusterings that are most consistent with known cell type labels.

Availability The open source library is at https://github.com/dnbaker/minicore. Code used for experiments is at https://github.com/dnbaker/minicore-experiments.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/dnbaker/minicore-experiments

Copyright 
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 4.0 International license.
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Posted March 25, 2021.
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minicore: Fast scRNA-seq clustering with various distances
Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead
bioRxiv 2021.03.24.436859; doi: https://doi.org/10.1101/2021.03.24.436859
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minicore: Fast scRNA-seq clustering with various distances
Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead
bioRxiv 2021.03.24.436859; doi: https://doi.org/10.1101/2021.03.24.436859

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