Methods in Ecology and Evolution
1. Automated, ship-board flow cytometers provide high-resolution maps of phytoplankton composition over large swaths of the world's oceans. They therefore pave the way for understanding how environmental conditions shape community structure. Identification of community changes along a cruise transect commonly segments the data into distinct regions. However, existing segmentation methods are generally not applicable to flow cytometry data, as these data are recorded as ‘point cloud’ data, with hundreds or thousands of particles measured during each time interval. Moreover, nonparametric segmentation methods that do not rely on prior knowledge of the number of species are desirable to map community shifts.
2. We present CytoSegmenter, a kernel-based change-point estimation method for segmenting point cloud data. Our method allows us to represent and summarize a point cloud of data points by a single element in a Hilbert space. The change-point locations can be found using a fast dynamic programming algorithm.
3. Through an analysis of 12 cruises, we demonstrate that CytoSegmenter allows us to locate abrupt changes in phytoplankton community structure. We show that the changes in community structure generally coincide with changes in the temperature and salinity of the ocean. We also illustrate how the main parameter of CytoSegmenter can be easily calibrated using limited auxiliary annotated data.
4. CytoSegmenter is generally applicable for segmenting series of point cloud data from any domain. Moreover, it readily scales to thousands of point clouds, each containing thousands of points. In the context of flow cytometry data collected during research cruises, it does not require prior clustering of particles to define taxa labels, eliminating a potential source of error. This represents an important advance in automating the analysis of large datasets now emerging in biological oceanography and other fields. It also allows for the approach to be applied during research cruises.
© 2021 The Authors.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Original Publication Citation
Jones, C., Clayton, S., Ribalet, F., Armbrust, E. V., & Harchaoui, Z. (2021). A kernel-based change detection method to map shifts in phytoplankton communities measured by flow cytometry. Methods in Ecology and Evolution, 12(9), 1687-1698. https://doi.org/10.1111/2041-210X.13647
Jones, Corinne; Clayton, Sophie; Ribalet, François; Armbrust, E. Virginia; and Harchaoui, Zaid, "A Kernel-Based Change Detection Method to Map Shifts in Phytoplankton Communities Measured by Flow Cytometry" (2021). OES Faculty Publications. 418.