caa a22 4500 528786091 20190713030956.0 cr unu---uuuuu 180924s2017 xx s 000 0 eng 10.3929/ethz-b-000130442 doi 10.1038/ncomms14825 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/130442 Arvaniti Eirini Sensitive detection of rare disease-Associated cell subsets via representation learning [Elektronische Daten] [Eirini Arvaniti, Manfred Claassen] Nat Commun Open access ethresearch 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%. Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 ethresearch Claassen Manfred joint author Nature Communications London : Nature Publishing Group 8, p. 14825 2041-1723 http://hdl.handle.net/20.500.11850/130442 text/html WWW-Backlink auf das Repository (Open access) 1 Journal Article ethresearch ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/130442 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 100 1- Arvaniti Eirini ETHRESEARCH 700 1- Claassen Manfred joint author ETHRESEARCH 773 0- Nature Communications London : Nature Publishing Group 8, p. 14825 2041-1723 BK010053 XK010053 XK010000 ETHRESEARCH ETHRESEARCH ETHRESEARCH Journal Article Open access