Fig. 4: PSS features.
From: Perisomatic ultrastructure efficiently classifies cells in mouse cortex
a, Inhibitory neurons exhibit great variability in ultrastructural morphology. b, Procedure for building a PSS dictionary model. The set of shapes is used to train a PointNet autoencoder that learns a latent feature vector of a fixed size (1,024). This autoencoder is then applied to all shapes in the dictionary to generate a set of latent feature vectors. k-means with k = 30 is applied to this to obtain a set of cluster centres for binning the shapes. c, For each cell, the PSSs are binned by shape type and distance from the soma (4 bins) from 0 to 60 µm with 15 µm bin sizes. The resulting histogram is a 2D histogram shown above with the shapes in the x direction and distances in the y direction. d, Examples of 60-µm cutouts of the four predicted inhibitory subclasses with their spatial histograms shown as heat maps. The top row shows the shape of the cluster centre of each of the 30 clusters. In each heat map, darker rectangles indicate higher values. e, z-scored feature matrix representing the distance-binned PSS features on the manually labelled inhibitory cells from the cortical column (n = 143). Cells are organized by their annotated subclass. Tick marks along the x axis denote segments of 100 cells. f, 2D UMAP of all the inhibitory neurons (n = 6,805) inferred after concatenating nucleus, soma and PSS features, with cortical column cells in colour and dataset-wide inhibitory neurons in grey.
