Efficient Integration of Heterogeneous Single-cell Transcriptomes Using Scanorama

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2019 May 8

PMID 31061482

Citations 354

Authors

Brian Hie

Bryan Bryson

Bonnie Berger

Affiliations

Soon will be listed here.

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.

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References

Zappia L, Phipson B, Oshlack A . Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017; 18(1):174. PMC: 5596896. DOI: 10.1186/s13059-017-1305-0. View

Baron M, Veres A, Wolock S, Faust A, Gaujoux R, Vetere A . A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst. 2016; 3(4):346-360.e4. PMC: 5228327. DOI: 10.1016/j.cels.2016.08.011. View

Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner H . Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017; 14(10):979-982. PMC: 5764547. DOI: 10.1038/nmeth.4402. View

Kiselev V, Yiu A, Hemberg M . scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. 2018; 15(5):359-362. DOI: 10.1038/nmeth.4644. View

Ronen J, Akalin A . netSmooth: Network-smoothing based imputation for single cell RNA-seq. F1000Res. 2018; 7:8. PMC: 5814748. DOI: 10.12688/f1000research.13511.3. View

Soneson C, Robinson M . Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018; 15(4):255-261. DOI: 10.1038/nmeth.4612. View

Haghverdi L, Lun A, Morgan M, Marioni J . Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018; 36(5):421-427. PMC: 6152897. DOI: 10.1038/nbt.4091. View

Loh P, Baym M, Berger B . Compressive genomics. Nat Biotechnol. 2012; 30(7):627-30. DOI: 10.1038/nbt.2241. View

Treutlein B, Brownfield D, Wu A, Neff N, Mantalas G, Espinoza F . Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature. 2014; 509(7500):371-5. PMC: 4145853. DOI: 10.1038/nature13173. View

10.

Yip S, Sham P, Wang J . Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief Bioinform. 2018; 20(4):1583-1589. PMC: 6781572. DOI: 10.1093/bib/bby011. View

11.

Ding J, Condon A, Shah S . Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun. 2018; 9(1):2002. PMC: 5962608. DOI: 10.1038/s41467-018-04368-5. View

12.

Satija R, Farrell J, Gennert D, Schier A, Regev A . Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015; 33(5):495-502. PMC: 4430369. DOI: 10.1038/nbt.3192. View

13.

Cleary B, Cong L, Cheung A, Lander E, Regev A . Efficient Generation of Transcriptomic Profiles by Random Composite Measurements. Cell. 2017; 171(6):1424-1436.e18. PMC: 5726792. DOI: 10.1016/j.cell.2017.10.023. View

14.

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View

15.

Hie B, Cho H, DeMeo B, Bryson B, Berger B . Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. Cell Syst. 2019; 8(6):483-493.e7. PMC: 6597305. DOI: 10.1016/j.cels.2019.05.003. View

16.

Shalek A, Satija R, Shuga J, Trombetta J, Gennert D, Lu D . Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014; 510(7505):363-9. PMC: 4193940. DOI: 10.1038/nature13437. View

17.

Tung P, Blischak J, Hsiao C, Knowles D, Burnett J, Pritchard J . Batch effects and the effective design of single-cell gene expression studies. Sci Rep. 2017; 7:39921. PMC: 5206706. DOI: 10.1038/srep39921. View

18.

Buttner M, Miao Z, Wolf F, Teichmann S, Theis F . A test metric for assessing single-cell RNA-seq batch correction. Nat Methods. 2018; 16(1):43-49. DOI: 10.1038/s41592-018-0254-1. View

19.

Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N . Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature. 2015; 525(7568):251-5. DOI: 10.1038/nature14966. View

20.

Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z . GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009; 10:48. PMC: 2644678. DOI: 10.1186/1471-2105-10-48. View