Fig. 4

Sfaira allows fitting of cell type classifiers for data sets with different levels of annotation granularity by using cell type ontologies. a Aggregated accuracy and cross-entropy allow for fitting cell type classification models on data sets with heterogeneous annotation coarsity using cell type relations from ontologies (see the “Methods” section). The y axis contains leaf nodes of a cell type ontology, which can be combined linearly to yield the predicted probability mass of any other node in the ontology graph (x-axis). b, c Accuracy of cell type classifiers on mouse (b) and human (c) organs on entirely held-out test-data sets. Linear: Linear classifier (logistic regression), marker: Marker gene-based classifier, MLP: multilayer dense neural network. d, e Class-wise prediction accuracy correlates with the number of cells in class. Shown are cell type class-wise F1 scores by the number of cell types in that class of cell type classifiers by model on lung data from mice (d) and humans (e)