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

Eirini Arvaniti and Manfred Claassen ()
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Eirini Arvaniti: Institute for Molecular Systems Biology
Manfred Claassen: Institute for Molecular Systems Biology

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: 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%.

Date: 2017
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DOI: 10.1038/ncomms14825

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