Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Rahul Satija · Full author list: Stuart T, Butler A (co-first), Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R (senior).

Published 13 June 2019 · Cell · Journal article

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Summary

This paper presents the Seurat v3 framework for integrating single-cell datasets across different technologies, conditions, and modalities. It introduces 'anchors' — pairs of cells in a shared low-dimensional space representing a common biological state — to harmonize datasets and transfer labels. The methods enable joint analysis of scRNA-seq with other measurements such as protein (CITE-seq), chromatin accessibility, and spatial data.

Key findings

Introduced anchor-based integration to align heterogeneous single-cell datasets.
Enabled label/annotation transfer from reference datasets onto query datasets.
Supported cross-modality integration (RNA, protein, ATAC, spatial) within the Seurat ecosystem.

Subjects & keywords

Biology & Genetics single-cell data integration seurat anchors multi-omics

Cite this paper

APA

Tim Stuart, Andrew Butler, & Rahul Satija [Full author list: Stuart T, Butler A (co-first), Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R (senior).] (2019). Comprehensive Integration of Single-Cell Data. Cell. https://doi.org/10.1016/j.cell.2019.05.031

BibTeX

@article{stuart2019comprehensive,
  author    = {Tim Stuart and Andrew Butler and Rahul Satija and {Full author list: Stuart T, Butler A (co-first), Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R (senior).}},
  title     = {Comprehensive Integration of Single-Cell Data},
  journal   = {Cell},
  year      = {2019},
  doi       = {10.1016/j.cell.2019.05.031},
  url       = {https://doi.org/10.1016/j.cell.2019.05.031}
}

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