Sensitive detection of rare disease-Associated cell subsets via representation learning
Gespeichert in:
Verfasser / Beitragende:
[Eirini Arvaniti, Manfred Claassen]
Ort, Verlag, Jahr:
2017
Enthalten in:
Nature Communications, 8, p. 14825
Format:
Artikel (online)
Online Zugang:
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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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| 100 | 1 | |a Arvaniti |D Eirini | |
| 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%. | |
| 540 | |a Creative Commons Attribution 4.0 International |u http://creativecommons.org/licenses/by/4.0 |2 ethresearch | ||
| 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 100 |E 1- |a Arvaniti |D Eirini | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Claassen |D Manfred |e joint author | ||
| 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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