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.
Original language | English (US) |
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Pages (from-to) | 1888-1902.e21 |
Journal | Cell |
Volume | 177 |
Issue number | 7 |
DOIs | |
State | Published - Jun 13 2019 |
Keywords
- integration
- multi-modal
- scATAC-seq
- scRNA-seq
- single cell
- single-cell ATAC sequencing
- single-cell RNA sequencing
ASJC Scopus subject areas
- General Biochemistry, Genetics and Molecular Biology