CoChat
Sign Up
Cited by 974

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

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

Published version of a preprint

Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41587-019-0113-3

Cites

27 of 27 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 27 references

Research works printed but not linked: 32

  1. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017). · DOI · Paper page
  2. Brown, M. & Lowe, D. G. Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74, 59–73 (2007). · DOI
  3. Dekel, T., Oron, S., Rubinstein, M., Avidan, S. & Freeman, W. T. Best-Buddies Similarity for robust template matching. in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (eds. Grauman, K. et al.) 2021–2029 (IEEE, 2015). · DOI
  4. Halko, N., Martinsson, P.-G. & Tropp, J. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev. 53, 217–288 (2011). · DOI
  5. Charikar, M. S. Similarity estimation techniques from rounding algorithms. in Proc. Thirty-Fourth Annual ACM Symposium on Theory of Computing (ed. Reif, J.) 380–388 (ACM, 2002). · DOI
  6. Dasgupta, S. & Freund, Y. Random projection trees and low dimensional manifolds. in Proc. Fourtieth Annual ACM Symposium on Theory of Computing (ed. Ladner, R. & Dwork, C.) 537–546 (ACM, 2008). · DOI
  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
  26. Van Der Maaten, L. J. P. & Hinton, G. E. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  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
  30. Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. in Proc. 7th Python Sci. Conf. (ed. Varoquaux, G. et al.) 11–15 (SciPy, 2008).
  31. 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 10, 48 (2009). · DOI · Paper page
  32. Skipper, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. in Proc. 9th Python Sci. Conf. (eds. van der Walt, S. & Millman, J.) 57–61 (SciPy, 2010).

Cited by

See all 974

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)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. https://app.cochat.ai/papers/10.1038/s41587-019-0113-3
The paper
Hie, B., Bryson, B., & Berger, B. (2019). Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nature Biotechnology. https://doi.org/10.1038/s41587-019-0113-3