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cyanoFilter: An R package to identify phytoplankton populations from flow cytometry data using cell pigmentation and granularity

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  • Olusoji, Oluwafemi D.
  • Spaak, Jurg W.
  • Holmes, Mark
  • Neyens, Thomas
  • Aerts, Marc
  • De Laender, Frederik

Abstract

Flow cytometry is often employed in ecology to measure traits and population size of bacteria and phytoplankton. This technique allows measuring millions of particles in a relatively small amount of time. However, distinguishing between different populations is not a straightforward task. Gating is a process in the identification of particles measured in flow cytometry. Gates can either be created manually using known characteristics of these particles, or by using automated clustering techniques. Available automated techniques implemented in statistical packages for flow cytometry are primarily developed for medicinal applications, while only two exist for phytoplankton. cyanoFilter is an R package built to identify phytoplankton populations from flow cytometry data. The package also integrates gating functions from two other automated algorithms. It also provides a gating accuracy test function that can be used to determine the accuracy of a desired gating function if monoculture flowcytometry data is available. The central algorithm in the package exploits observed pigmentation and granularity of phytoplankton cells. We demonstrate how its performance depends on strain similarity, using a model system of six cyanobacteria strains. Using the same system, we compare the performance of the central gating function in the package to similar functions in other packages.

Suggested Citation

  • Olusoji, Oluwafemi D. & Spaak, Jurg W. & Holmes, Mark & Neyens, Thomas & Aerts, Marc & De Laender, Frederik, 2021. "cyanoFilter: An R package to identify phytoplankton populations from flow cytometry data using cell pigmentation and granularity," Ecological Modelling, Elsevier, vol. 460(C).
    • Handle: RePEc:eee:ecomod:v:460:y:2021:i:c:s030438002100291x
    DOI: 10.1016/j.ecolmodel.2021.109743
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    References listed on IDEAS

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    1. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October.
    2. Pereira, G.C. & Andrade, L.P. & Espíndola, R.P. & Ebecken, N.F.F., 2019. "Ecological networks simulation by fuzzy ecotoxicological rules," Ecological Modelling, Elsevier, vol. 409(C), pages 1-1.
    3. Maayke Stomp & Jef Huisman & Floris de Jongh & Annelies J. Veraart & Daan Gerla & Machteld Rijkeboer & Bas W. Ibelings & Ute I. A. Wollenzien & Lucas J. Stal, 2004. "Adaptive divergence in pigment composition promotes phytoplankton biodiversity," Nature, Nature, vol. 432(7013), pages 104-107, November.
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