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

Verfasser / Beitragende:
[Eirini Arvaniti, Manfred Claassen]
Ort, Verlag, Jahr:
2017
Enthalten in:
Nature Communications, 8, p. 14825
Format:
Artikel (online)
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024 7 0 |a 10.3929/ethz-b-000130442  |2 doi 
024 7 0 |a 10.1038/ncomms14825  |2 doi 
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245 1 0 |a Sensitive detection of rare disease-Associated cell subsets via representation learning  |h [Elektronische Daten]  |c [Eirini Arvaniti, Manfred Claassen] 
246 0 |a Nat Commun 
506 |a Open access  |2 ethresearch 
520 3 |a 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%. 
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700 1 |a Claassen  |D Manfred  |e joint author 
773 0 |t Nature Communications  |d London : Nature Publishing Group  |g 8, p. 14825  |x 2041-1723 
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950 |B ETHRESEARCH  |P 773  |E 0-  |t Nature Communications  |d London : Nature Publishing Group  |g 8, p. 14825  |x 2041-1723 
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