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Fig. 2
Feature-based extraction of groups of interest.
(A) Quantitative features associated with each of the cell tracks allows feature-based selections. The left and right part of an embryo was separated along the anteroposterior axis using the end point coordinates of each track. (A1) and (A2) show the identified groups of ∼1400 neural crest cells as a scatter plot (x coordinate of the trajectory end points versus the unique track ID) and a 3D rendering of all trajectories. (B) Cluster algorithms can be used to automatically group the data. The example shows four identified clusters using the end point locations in the XY-plane at a selected time point as a 3D rendering (B1) and a scatter plot (B2). (C) Special trajectory features can be used to characterize particular cell movements in the early embryo. (C1) schematically illustrates the ratio of effective displacement (distance between start and end point) versus the spatial length (integrated path length) that was used to automatically identify two clusters corresponding to hypoblast cells (magenta) and epiblast cells (green) visualized as scatter plot (C2) and 3D rendering (C3). Panels (A) and (B) show neural crest cells of a zebrafish embryo (12.5 − 28 hpf) and panel (C) is based on a slice cut from a whole-embryo zebrafish data set (5 − 7.25 hpf). Scale bar: 100 μm.