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

Arvaniti, E., Claassen, M.

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%.

Citation

Arvaniti, E., Claassen, M. "Sensitive detection of rare disease-associated cell subsets via representation learning" Nature Communications (2017): doi: 10.1007/s12094-018-02004-8