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mbkmeans: Fast clustering for single cell data using mini-batch k-means

Author

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  • Stephanie C Hicks
  • Ruoxi Liu
  • Yuwei Ni
  • Elizabeth Purdom
  • Davide Risso

Abstract

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.Author summary: We developed the mbkmeans package (https://bioconductor.org/packages/mbkmeans) in Bioconductor, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time.

Suggested Citation

  • Stephanie C Hicks & Ruoxi Liu & Yuwei Ni & Elizabeth Purdom & Davide Risso, 2021. "mbkmeans: Fast clustering for single cell data using mini-batch k-means," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-18, January.
  • Handle: RePEc:plo:pcbi00:1008625
    DOI: 10.1371/journal.pcbi.1008625
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    References listed on IDEAS

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    1. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
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    Cited by:

    1. Linhua Wang & Mirjana Maletic-Savatic & Zhandong Liu, 2022. "Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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