After cell segmentation and QC, it is now possible to algorithmically classify each cell by phenotype. This is done using the biomarker expression values that are stored in the table of cells generated by segmentation.
There are two major methods for determining cell phenotype. The first method described here, Unsupervised Clustering, is automated and recommended for first-pass phenotyping since it can efficiently group cells with similar biomarker expression levels using all of the biomarker expression data at once. The second method, Gating, is more manual, relying on the user to define specific hierarchies for cell classification. Both methods are supported through the Enable Medicine Portal.
The end result of a phenotyping workflow will assign each cell that passed QC a phenotype label, which can then be used for spatial analysis.