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Computational flow cytometry: helping to make sense of high-dimensional immunology data

Yvan Saeys (UGent) , Sofie Van Gassen (UGent) and Bart Lambrecht (UGent)
(2016) NATURE REVIEWS IMMUNOLOGY. 16(7). p.449-462
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Abstract
Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.
Keywords
CELL-POPULATION IDENTIFICATION, HUMAN B-CELL, MASS CYTOMETRY, AUTOMATED IDENTIFICATION, HEMATOPOIETIC-CELLS, REVEALS, DISCOVERY, VISUALIZATION, BIOCONDUCTOR, SUBSETS

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MLA
Saeys, Yvan, Sofie Van Gassen, and Bart Lambrecht. “Computational Flow Cytometry: Helping to Make Sense of High-dimensional Immunology Data.” NATURE REVIEWS IMMUNOLOGY 16.7 (2016): 449–462. Print.
APA
Saeys, Y., Van Gassen, S., & Lambrecht, B. (2016). Computational flow cytometry: helping to make sense of high-dimensional immunology data. NATURE REVIEWS IMMUNOLOGY, 16(7), 449–462.
Chicago author-date
Saeys, Yvan, Sofie Van Gassen, and Bart Lambrecht. 2016. “Computational Flow Cytometry: Helping to Make Sense of High-dimensional Immunology Data.” Nature Reviews Immunology 16 (7): 449–462.
Chicago author-date (all authors)
Saeys, Yvan, Sofie Van Gassen, and Bart Lambrecht. 2016. “Computational Flow Cytometry: Helping to Make Sense of High-dimensional Immunology Data.” Nature Reviews Immunology 16 (7): 449–462.
Vancouver
1.
Saeys Y, Van Gassen S, Lambrecht B. Computational flow cytometry: helping to make sense of high-dimensional immunology data. NATURE REVIEWS IMMUNOLOGY. 2016;16(7):449–62.
IEEE
[1]
Y. Saeys, S. Van Gassen, and B. Lambrecht, “Computational flow cytometry: helping to make sense of high-dimensional immunology data,” NATURE REVIEWS IMMUNOLOGY, vol. 16, no. 7, pp. 449–462, 2016.
@article{8042745,
  abstract     = {Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.},
  author       = {Saeys, Yvan and Van Gassen, Sofie and Lambrecht, Bart},
  issn         = {1474-1733},
  journal      = {NATURE REVIEWS IMMUNOLOGY},
  keywords     = {CELL-POPULATION IDENTIFICATION,HUMAN B-CELL,MASS CYTOMETRY,AUTOMATED IDENTIFICATION,HEMATOPOIETIC-CELLS,REVEALS,DISCOVERY,VISUALIZATION,BIOCONDUCTOR,SUBSETS},
  language     = {eng},
  number       = {7},
  pages        = {449--462},
  title        = {Computational flow cytometry: helping to make sense of high-dimensional immunology data},
  url          = {http://dx.doi.org/10.1038/nri.2016.56},
  volume       = {16},
  year         = {2016},
}

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