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A Generative Statistical Algorithm for Automatic Detection of Complex Postures

Fig 6

(A) The leading four PCA modes for individual coiler mutants (thin lines) and for the combined coiler dataset (thick lines). (B) Left: the variance explained by the modes of the combined coiler dataset in order of their significance. Dashed line represents 95%. Middle/right: centroids of posture clusters were projected onto the plane of the two leading modes. Typical coiling postures were reconstructed from centroids. The intuitive interpretation of the two leading modes is demonstrated by the separation between ventral (positive amplitudes) and dorsal (negative amplitudes) coiling. (C) The dynamics of the amplitudes of the three leading modes during continuous periods of coiling. The duration of individual coiling events were normalized such that the horizontal axis depicts the fraction of duration of the coiling event (mean durations for each strain can be seen in Fig 5A). (D) Left: for each strain shown, the 10 most populated cluster centroids for spools (coiled postures for which a1·a2 > 1) were projected onto the plane of the two leading modes and their convex hull was calculated. These convex hulls for wild-type animals (red), static coilers (blue shades) and loopy movers (orange shades) are shown in the plane of the two leading modes. Example postures were reconstructed from the cluster centroids (blue curves). Dotted lines point from the position of a cluster centroid to the reconstruction of its respective body posture. Grey circles at edges of reconstructed postures denote the position of the head. Right: the fraction of severe spools exhibited by wild-type animals and coiler mutants in our assays. Error bars and thin lines depict animal-to-animal variation (mean ± s.e.m). Eigenworms are represented using angle differences (49) as opposed to angles with a fixed axis (7) (see Materials and Methods).

Fig 6

doi: https://doi.org/10.1371/journal.pcbi.1004517.g006