Fig. 3: Automated deep cell phenotyping from multiplexed imaging using TYPEx.

a Using a cell-by-marker intensity matrix and cell-type defining config file as input, TYPEx performs four steps: cell stratification through combination of existing methods (1–3), marker positivity detection, cell-type assignment and tissue segmentation. b To determine marker positivity, each cluster derived from the cell-stratification step is compared pairwise with all other clusters, and for each marker, the probability that a random cell from the given cluster has a higher intensity of that marker than cells from another cluster is calculated. Examples of probability distributions for a cluster A expressing a given marker (top left) and a cluster B that does not express that marker (top right) are illustrated. The D-score represents the maximum positive distance from the cumulative to the background distribution (bottom). c For each confidence group and across all possible D-score cutoffs (0–1 range, step 0.0001), the number of cells expressing a combination of user-provided markers is calculated. c illustrates an example for the high-confidence group in the T cells & Stroma panel using the default T-cell markers, based on which, three types of T-cell populations are defined: rare, dominant, and variable (vary depending on the dataset). The optimal cutoff minimises the rare (left) and maximises the dominant (right) T-cell subpopulations. d The ratio of rare to dominant T-cell populations against the range of D-score cutoffs. The cutoff range in which any of the dominant populations has zero cell count is not considered (grey area). At the lowest values of the D-score cutoff, the number of double-positive (CD3^+/−)CD8a⁺CD4⁺ T cells and overall Ambiguous cells increases; as the D-score cutoff exceeds the optimal, the number of single-positive CD3⁻CD8a⁺ T (overall Unassigned cells) increases. The optimal D-score cutoff, shown with a vertical black line (c, d), is determined individually for the low- and high-confidence groups in each study and panel. e To output cell densities, TYPEx uses a random forest classifier for tissue segmentation, a user-specific model, or binary masks of tissue domains as input. Source data are provided as a Source Data file.