TY - JOUR
T1 - cyanoFilter
T2 - An R package to identify phytoplankton populations from flow cytometry data using cell pigmentation and granularity
AU - Olusoji, Oluwafemi D.
AU - Spaak, Jurg W.
AU - Holmes, Mark
AU - Neyens, Thomas
AU - Aerts, Marc
AU - De Laender, Frederik
N1 - Funding Information:
This work was supported by funds from the bilateral co-operation (BOF) between Hasselt University and Université de Namur . F.D.L. was supported by the ARC, Belgium grant DIVERCE, a concerted research action from the special research fund (Convention no 18/23-095 ), and by the Fund for Scientific Research, FNRS, Belgium ( PDR T.0048.16 ).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/15
Y1 - 2021/11/15
N2 - 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.
AB - 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.
KW - Ecology
KW - Flow cytometry
KW - Gating
KW - Phytoplankton
KW - Software
UR - http://www.scopus.com/inward/record.url?scp=85116000162&partnerID=8YFLogxK
U2 - 10.1016/j.ecolmodel.2021.109743
DO - 10.1016/j.ecolmodel.2021.109743
M3 - Article
AN - SCOPUS:85116000162
SN - 0304-3800
VL - 460
JO - Ecological Modelling
JF - Ecological Modelling
M1 - 109743
ER -