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Nat Biotechnol. 2019 Jun;37(6):685-691. doi: 10.1038/s41587-019-0113-3. Epub 2019 May 6.

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama.

Author information

1
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
2
Department of Biological Engineering, MIT, Cambridge, MA, USA. bryand@mit.edu.
3
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA. bab@mit.edu.
4
Department of Mathematics, MIT, Cambridge, MA, USA. bab@mit.edu.

Abstract

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data. We applied Scanorama to integrate and remove batch effects across 105,476 cells from 26 diverse scRNA-seq experiments representing 9 different technologies. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14+ monocytes at different stages of differentiation into macrophages. Finally, we show that Scanorama is orders of magnitude faster than existing techniques and can integrate a collection of 1,095,538 cells in just ~9 h.

PMID:
31061482
PMCID:
PMC6551256
[Available on 2019-12-01]
DOI:
10.1038/s41587-019-0113-3
[Indexed for MEDLINE]

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