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Abstract
Single-cell transcriptomics has transformed our ability to characterize cell states,
but deep biological understanding requires more than a taxonomic listing of clusters.
As new methods arise to measure distinct cellular modalities, a key analytical challenge
is to integrate these datasets to better understand cellular identity and function.
Here, we develop a strategy to "anchor" diverse datasets together, enabling us to
integrate single-cell measurements not only across scRNA-seq technologies, but also
across different modalities. After demonstrating improvement over existing methods
for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to
explore chromatin differences in closely related interneuron subsets and project protein
expression measurements onto a bone marrow atlas to characterize lymphocyte populations.
Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide
imputation of spatial gene expression patterns. Our work presents a strategy for the
assembly of harmonized references and transfer of information across datasets.