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Chunk #12 — Results — Mapping scATAC-seq data onto scRNA-seq references

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Dictionary learning for integrative, multimodal and scalable single-cell analysis.
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Our reference-derived annotations were concordant with the annotations accompanying the query dataset produced by the original authors (Supplementary Fig. 1b), but we found that bridge integration annotated additional rare and high-resolution subpopulations. For example, our annotations separated monocytes into CD14+ and CD16+ fractions, NK cells into CD56bright and CD56dim subgroups, and cytotoxic T cells into CD8+ and mucosal associated invariant T (MAIT) subpopulations (Fig. 2d,e and Supplementary Fig. 1c). While these subdivisions were not identified in the unsupervised scATAC-seq analysis, we confirmed these predictions by observing differential accessibility at canonical loci (i.e. elevated accessibility at the FCGR3A/CD16 gene locus in CD16+ monocytes), after grouping by reference-derived annotations. We validated these chromatin patterns using independent multiome datasets, where cell identity was assigned based on concurrent RNA measurements (Supplementary Fig. 1d,e). Similarly, bridge integration identified extremely rare groups of innate lymphoid cells (ILC; 0.15%), and recently discovered AXL+SIGLEC6+ (ASDC) dendritic cells45,46 (0.10%) (Fig. 2f and Supplementary Fig. 1c). To our knowledge, these cell populations have not been previously identified in scATAC-seq data. Again, we found that differentially accessible sites, such as an ASDC-specific peak in the SIGLEC6 gene (Fig. 2f), fully supported the accuracy of our mapping procedure.