NEURON DENDRITE INTERIOR PC
The closer a parameter is to 1, the more it contributed to the separation in the PC space, in the direction indicated by its position within the unit circle a parameter located at the origin did not contribute to the PC separation. Numbers in circles correspond to the measures as listed in Table 1. c Relative contributions of the 25 morphometric parameters to the principal component separation in the Alexa-labeled (left) and Biocytin-labeled (right) data sets. Nonetheless, the classification results are qualitatively similar in all three data sets in that the grouping assigned by the K-means algorithm draws a distinguishing line almost perpendicular to the principal component most strongly correlated to straightness. In contrast, only PC2 is significantly correlated with straightness in the Biocytin-labeled data. In the Alexa-labeled data the first two principal components of separation are both correlated with straightness. b Correlation between “straightness” and the first (top panel) and second (bottom panel) principal component of separation for the Alexa-labeled (left) and Biocytin-labeled (right) data sets.
In the Alexa-labeled data, as in the viral-labeled data, the separation into distinct groups (as determined by the K-means algorithm) spreads along PC1, whereas in the Biocytin-labeled data, the distinction is better represented along PC2.
The grey dashed line marks the algorithmically forced K-means grouping into “curly” and “straight” fill color represents subjective classification, as indicated. a Algorithmic classification shown as a scatter along the first two principal components (PCs) of separation for the Alexa-labeled (left) and Biocytin-labeled (right) data sets.
Supplementary material 2 Supplementary Figure 2 (supplement to Figure 3) Algorithmic classification does not reveal clearly separated clusters in two data sets of patch-filled morphologies.
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