Sensitive detection of rare disease-associated cell subsets via representation learning.
- Arvaniti E Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Auguste-Piccard-Hof 1, Zurich 8093, Switzerland.
- Claassen M Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Auguste-Piccard-Hof 1, Zurich 8093, Switzerland.
- 2017-04-07
Published in:
- Nature communications. - 2017
Acquired Immunodeficiency Syndrome
Cytokines
Cytomegalovirus Infections
Humans
Immunologic Memory
Killer Cells, Natural
Leukemia
Monocytes
Neoplasm, Residual
Neural Networks, Computer
Prognosis
Rare Diseases
Signal Transduction
Single-Cell Analysis
Supervised Machine Learning
Survival Analysis
T-Lymphocyte Subsets
English
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%.
- Language
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- English
- Open access status
- gold
- Identifiers
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- DOI 10.1038/ncomms14825
- PMID 28382969
- Persistent URL
- https://sonar.rero.ch/global/documents/199752
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