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Comprehensive integration of single cell data

View ORCID ProfileTim Stuart, View ORCID ProfileAndrew Butler, View ORCID ProfilePaul Hoffman, View ORCID ProfileChristoph Hafemeister, View ORCID ProfileEfthymia Papalexi, William M. Mauck III, View ORCID ProfileMarlon Stoeckius, View ORCID ProfilePeter Smibert, View ORCID ProfileRahul Satija
doi: https://doi.org/10.1101/460147
Tim Stuart
1New York Genome Center, New York City, NY, USA
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Andrew Butler
1New York Genome Center, New York City, NY, USA
2Center for Genomics and Systems Biology, New York University, New York City, NY, USA
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Paul Hoffman
1New York Genome Center, New York City, NY, USA
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Christoph Hafemeister
1New York Genome Center, New York City, NY, USA
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Efthymia Papalexi
1New York Genome Center, New York City, NY, USA
2Center for Genomics and Systems Biology, New York University, New York City, NY, USA
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William M. Mauck III
1New York Genome Center, New York City, NY, USA
2Center for Genomics and Systems Biology, New York University, New York City, NY, USA
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Marlon Stoeckius
3Technology Innovation Lab, New York Genome Center, New York City, NY, USA
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  • ORCID record for Marlon Stoeckius
Peter Smibert
3Technology Innovation Lab, New York Genome Center, New York City, NY, USA
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Rahul Satija
1New York Genome Center, New York City, NY, USA
2Center for Genomics and Systems Biology, New York University, New York City, NY, USA
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  • For correspondence: rsatija@nygenome.org
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Abstract

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets.

Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 02, 2018.
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Comprehensive integration of single cell data
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M. Mauck III, Marlon Stoeckius, Peter Smibert, Rahul Satija
bioRxiv 460147; doi: https://doi.org/10.1101/460147
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Comprehensive integration of single cell data
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M. Mauck III, Marlon Stoeckius, Peter Smibert, Rahul Satija
bioRxiv 460147; doi: https://doi.org/10.1101/460147

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