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Sensitive detection of rare disease-associated cell subsets via representation learning

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  • Eirini Arvaniti

    (Institute for Molecular Systems Biology
    Swiss Institute of Bioinformatics
    Life Science Graduate School Zurich, PhD Program Systems Biology)

  • Manfred Claassen

    (Institute for Molecular Systems Biology
    Swiss Institute of Bioinformatics)

Abstract

Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%.

Suggested Citation

  • Eirini Arvaniti & Manfred Claassen, 2017. "Sensitive detection of rare disease-associated cell subsets via representation learning," Nature Communications, Nature, vol. 8(1), pages 1-10, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14825
    DOI: 10.1038/ncomms14825
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    Cited by:

    1. Ross J Burton & Raya Ahmed & Simone M Cuff & Sarah Baker & Andreas Artemiou & Matthias Eberl, 2021. "CytoPy: An autonomous cytometry analysis framework," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-21, June.
    2. Xiaoying Wang & Maoteng Duan & Jingxian Li & Anjun Ma & Gang Xin & Dong Xu & Zihai Li & Bingqiang Liu & Qin Ma, 2024. "MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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