Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Name
nihms-1021973.pdf
Description
Accepted version
Size
1.07 MB
Format
Adobe PDF
Checksum (MD5)
adc742d2891a9b1b3d46aaa6d5efe5d3
Author(s)
Hie, Brian
Bryson, Bryan D.
Berger Leighton, Bonnie
Date Issued
May 2019
Journal
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Citation
Hie, Brian et al. "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama." Nature Biotechnology (May 2019): 685–691 © 2019 Springer Nature
Version
Author's final manuscript
Abstract
ntegration 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/s41587-019-0113-3
