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SCHNEL: Scalable clustering of high dimensional single-cell data

View ORCID ProfileTamim Abdelaal, Paul de Raadt, View ORCID ProfileBoudewijn P.F. Lelieveldt, View ORCID ProfileMarcel J.T. Reinders, View ORCID ProfileAhmed Mahfouz
doi: https://doi.org/10.1101/2020.03.30.015925
Tamim Abdelaal
1Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
2Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
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  • ORCID record for Tamim Abdelaal
Paul de Raadt
2Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
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Boudewijn P.F. Lelieveldt
1Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
2Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
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Marcel J.T. Reinders
1Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
2Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
3Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
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Ahmed Mahfouz
1Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
2Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
3Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
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  • For correspondence: a.mahfouz@lumc.nl
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Abstract

Motivation Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.

Results We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.

Availability and Implementation Implementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering)

Contact a.mahfouz{at}lumc.nl

Supplementary information Supplementary data are available at Bioinformatics online.

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-NC 4.0 International license.
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Posted March 31, 2020.
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SCHNEL: Scalable clustering of high dimensional single-cell data
Tamim Abdelaal, Paul de Raadt, Boudewijn P.F. Lelieveldt, Marcel J.T. Reinders, Ahmed Mahfouz
bioRxiv 2020.03.30.015925; doi: https://doi.org/10.1101/2020.03.30.015925
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SCHNEL: Scalable clustering of high dimensional single-cell data
Tamim Abdelaal, Paul de Raadt, Boudewijn P.F. Lelieveldt, Marcel J.T. Reinders, Ahmed Mahfouz
bioRxiv 2020.03.30.015925; doi: https://doi.org/10.1101/2020.03.30.015925

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