Sensitive detection of rare disease-associated cell subsets via representation learning
Copyright policy )Sensitive detection of rare disease-associated cell subsets via representation learning
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%.
- ETH Zurich Switzerland
- Institute for Molecular Systems Biology Switzerland
- University of Lausanne Switzerland
- SIB Swiss Institute of Bioinformatics Switzerland
Acquired Immunodeficiency Syndrome, Leukemia, Neoplasm, Residual, Science, Q, Prognosis, Survival Analysis, Article, Monocytes, Killer Cells, Natural, Rare Diseases, T-Lymphocyte Subsets, Cytomegalovirus Infections, Cytokines, Humans, Neural Networks, Computer, Supervised Machine Learning, Single-Cell Analysis, Immunologic Memory, Signal Transduction
Acquired Immunodeficiency Syndrome, Leukemia, Neoplasm, Residual, Science, Q, Prognosis, Survival Analysis, Article, Monocytes, Killer Cells, Natural, Rare Diseases, T-Lymphocyte Subsets, Cytomegalovirus Infections, Cytokines, Humans, Neural Networks, Computer, Supervised Machine Learning, Single-Cell Analysis, Immunologic Memory, Signal Transduction
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