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

Open access
Date
2017Type
- Journal Article
Citations
Cited 64 times in
Web of Science
Cited 71 times in
Scopus
ETH Bibliography
yes
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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%. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000130442Publication status
publishedExternal links
Journal / series
Nature CommunicationsVolume
Pages / Article No.
Publisher
Nature Publishing GroupOrganisational unit
03984 - Claassen, Manfred (ehemalig) / Claassen, Manfred (former)
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Citations
Cited 64 times in
Web of Science
Cited 71 times in
Scopus
ETH Bibliography
yes
Altmetrics