Comprehensive integration of single-cell data

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 different modalities as well. 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.

Graphical Abstract