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

Nat Rev Immunol. 2016 Jul;16(7):449-62. doi: 10.1038/nri.2016.56. Epub 2016 Jun 20.


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.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Computational Biology / methods*
  • Flow Cytometry / methods*
  • Humans
  • Immunologic Techniques*