Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Author(s)Hie, Brian; Bryson, Bryan D.; Berger Leighton, Bonnie
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Biological Engineering; Massachusetts Institute of Technology. Department of Mathematics
Springer Science and Business Media LLC
Hie, Brian et al. "Efficient integration of heterogeneous single-cell transcriptomes using Scanorama." Nature Biotechnology (May 2019): 685–691 © 2019 Springer Nature
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