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      Comprehensive Integration of Single-Cell Data

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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.

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          Author and article information

          Journal
          Cell
          Cell
          Elsevier BV
          00928674
          June 2019
          June 2019
          Article
          10.1016/j.cell.2019.05.031
          6687398
          31178118
          9e539477-5f9a-4fb9-bf8e-c8b4cf3bba59
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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