title: 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%.
authors.affiliation: Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Auguste-Piccard-Hof 1, Zurich 8093, Switzerland.|Swiss Institute of Bioinformatics, Zurich 8093, Switzerland.|Life Science Graduate School Zurich, PhD Program Systems Biology, Winterthurerstr. 190, Zurich 8057, Switzerland.
authors.forename: Eirini
authors.identifier: 0000-0003-3045-9309
authors.initials: E
authors.lastname: Arvaniti
authors.affiliation: Institute for Molecular Systems Biology, Department of Biology, ETH Zurich, Auguste-Piccard-Hof 1, Zurich 8093, Switzerland.|Swiss Institute of Bioinformatics, Zurich 8093, Switzerland.
authors.forename: Manfred
authors.initials: M
authors.lastname: Claassen
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000276:immunology / Q000473:pathology; D016207:Cytokines / Q000494:pharmacology; D003586:Cytomegalovirus Infections / Q000276:immunology / Q000473:pathology; D006801:Humans; D007156:Immunologic Memory; D007694:Killer Cells, Natural / Q000187:drug effects / Q000276:immunology; D007938:Leukemia / Q000276:immunology / Q000473:pathology; D009000:Monocytes / Q000187:drug effects / Q000276:immunology; D018365:Neoplasm, Residual; D016571:Neural Networks, Computer; D011379:Prognosis; D035583:Rare Diseases / Q000473:pathology*; D015398:Signal Transduction; D059010:Single-Cell Analysis; D000069553:Supervised Machine Learning; D016019:Survival Analysis; D016176:T-Lymphocyte Subsets
chemical_list: D016207:Cytokines