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Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

Hie, Brian, Bryson, Bryan, Berger, Bonnie·2019·Nature Biotechnology
DOI 10.1038/s41587-019-0113-3PMC PMC6551256

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Research works printed but not linked: 32

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  7. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015). · DOI · Paper page
  8. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016). · DOI · Paper page
  9. Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016). · DOI
  10. Lawlor, N. et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 27, 208–222 (2017). · DOI
  11. Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). · DOI · Paper page
  12. Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998.e20 (2018). · DOI · Paper page
  13. Li, W. V. & Li, J. J. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9, 997 (2018). · DOI
  14. Ronen, J. & Akalin, A. netSmooth: Network-smoothing based imputation for single cell RNA-seq. F1000Research 7, 8 (2018). · DOI
  15. Yip, S. H., Sham, P. C. & Wang, J. Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief. Bioinform. https://doi.org/10.1093/bib/bby011 (2018). · DOI
  16. Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across datasets. Nat. Methods 15, 359–362 (2018). · DOI
  17. Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017). · DOI · Paper page
  18. Zhang, J. M., Fan, J., Fan, H. C., Rosenfeld, D. & Tse, D. N. An interpretable framework for clustering single-cell RNA-Seq datasets. BMC Bioinformatics 19, 93 (2018). · DOI
  19. Cho, H., Berger, B. & Peng, J. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7, 185–191 (2018). · DOI
  20. Van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018). · DOI · Paper page
  21. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018). · DOI · Paper page
  22. Cleary, B., Cong, L., Cheung, A., Lander, E. S. & Regev, A. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171, 1424–1436.e18 (2017). · DOI · Paper page
  23. Hie, B., Cho, H., DeMeo, B., Bryson, B. & Berger, B. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst. (in the press); preprint at https://doi.org/10.1101/536730 · DOI
  24. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017). · DOI
  25. Loh, P. R., Baym, M. & Berger, B. Compressive genomics. Nature Biotech. 30, 627–630 (2012). · DOI
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  27. Pedregosa F. & Varoquaux G. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
  28. Buttner, M., Miao, Z., Wolf, A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2017). · DOI · Paper page
  29. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). · DOI · Paper page
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Metadata

Authors
Hie, B., Bryson, B., & Berger, B.
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2019-05-06
Volume
37
Issue
6
Pages
685-691
ISSN
  • 1087-0156
  • 1546-1696
DOI
10.1038/s41587-019-0113-3
PMC
PMC6551256
Cited by
974
References
27
Language
en
Licence
www.springer.com/tdm (tdm)

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