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VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1038/s41467-020-17569-8
AuthorSearch for: ; Search for: ORCID identifier: https://orcid.org/0000-0002-9974-4661; Search for: ORCID identifier: https://orcid.org/0000-0002-2380-6424; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0003-4296-3481; Search for: ORCID identifier: https://orcid.org/0000-0001-9245-6686; Search for: ; Search for: 1; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0003-0083-6994; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0001-6394-3055; Search for: ; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0001-7061-5594; Search for: ; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0002-3450-4325; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0003-1205-6936; Search for: ; Search for: ; Search for: ; Search for: ORCID identifier: https://orcid.org/0000-0002-1550-8136; Search for: ORCID identifier: https://orcid.org/0000-0002-3475-5706; Search for: ORCID identifier: https://orcid.org/0000-0002-6117-8764
Affiliation
  1. National Research Council of Canada. Digital Technologies
FormatText, Article
Subjectapplied immunology; cellular signalling networks; computer modelling
Abstract
High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.
Publication date
PublisherNature Research
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  • Nature Communications 11, no. 1, 3738.
LanguageEnglish
Peer reviewedYes
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Record identifier2af21cbd-57ad-4f77-b94c-725472a42c25
Record created2021-06-01
Record modified2021-06-11
Date modified: