Extended Data Fig. 1: STELLAR overview.
From: Annotation of spatially resolved single-cell data with STELLAR
STELLAR is a unique method in its ability to simultaneously recognize cell types seen in the reference set and discover novel cell types that have never been characterized in the reference set. This is made possible by an objective function that consists of two main components (Methods). First, STELLAR learns to gradually separate cell types from the reference set by controlling intra-class variance to allow the model to simultaneously learn to discover novel cell types. Simultaneously, STELLAR discovers novel classes by generating auxiliary labels (pseudo-labels) in the unannotated graph that are used to guide the training. The auxiliary labels are generated based on the nearest neighbors of each cell in the embedding space.
