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dc.date.accessioned 2019-11-13T14:52:09Z
dc.date.available 2019-11-13T14:52:09Z
dc.date.issued 2013-09-05
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/85496
dc.description.abstract Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of "supercell statistics", a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques. en
dc.language en es
dc.subject Supercell statistics es
dc.subject Machine learning es
dc.subject Disease diagnosis es
dc.title From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells en
dc.type Articulo es
sedici.identifier.other doi:10.1371/journal.pcbi.1003215 es
sedici.identifier.other eid:2-s2.0-84884697284 es
sedici.identifier.issn 1553-734X es
sedici.creator.person Candia, Julián Marcelo es
sedici.creator.person Maunu, R. es
sedici.creator.person Driscoll, M. es
sedici.creator.person Biancotto, A. es
sedici.creator.person Dagur, P. es
sedici.creator.person McCoy Jr., J. P. es
sedici.creator.person Sen, H. N. es
sedici.creator.person Wei, L. es
sedici.creator.person Maritan, A. es
sedici.creator.person Cao, K. es
sedici.creator.person Nussenblatt, R. B. es
sedici.creator.person Banavar, J. R. es
sedici.creator.person Losert, W. es
sedici.subject.materias Biología es
sedici.subject.materias Ciencias Naturales es
sedici.description.fulltext true es
mods.originInfo.place Instituto de Física de Líquidos y Sistemas Biológicos es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle PLoS Computational Biology es
sedici.relation.journalVolumeAndIssue vol. 9, no. 9 es
sedici.rights.sherpa * Color: green * Pre-print del autor: si * Post-print del autor: si * Versión de editor/PDF:si * Condiciones: >>Creative Commons Attribution License 4.0 >>Authors retain copyright >>Publisher's version/PDF may be used >>Published source must be acknowledged with citation >>Author's pre-prints si be deposited in pre-print servers >>Publisher will deposit articles in PubMed Central >>All titles are open access journals * Link a Sherpa: http://sherpa.ac.uk/romeo/issn/1553-734X/es/


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)