Bridge integration annotated each CyTOF dataset with cluster labels derived from our 3.46M cell scRNA-seq collection, and allowed us to infer intracellular protein levels for each of these clusters (Fig. 5c). Predicted regulatory CD4+ T cells expressed high levels of the transcription factor Foxp366, and effector T cells exhibited enriched Klrg1 levels67 (Fig. 5d). We also found that among cytotoxic lymphocyte populations, MAIT cells were uniquely depleted for expression of the cytotoxic protease Granzyme B, consistent with previous reports68. Each of these patterns supports the accuracy of our cross-modality mapping. Finally, we successfully annotated a rare populations of innate lymphoid cells (0.024%), which were not independently identified in the CyTOF dataset, but correctly exhibited a CD25+CD127+CD161+CD56− immunophenotype4,69 (Fig. 5d,e). Taken together, we conclude that dictionary learning enhances the scalability of integration, as well as the ability to integrate and compare diverse molecular modalities.
