Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data

Nature Methods

Volumn 16, Issue 3, 2019, Pages 243-245

  Linderman, George C ^a   Rachh, Manas ^a   Hoskins, Jeremy G ^a   Steinerberger, Stefan ^a   Kluger, Yuval ^a,b  

^a YALE UNIVERSITY   (United States)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CELL POPULATION; EMBEDDING; GENE EXPRESSION; HUMAN; RNA SEQUENCE; SOFTWARE; STOCHASTIC MODEL; ALGORITHM; ANIMAL; BIOLOGY; GENE EXPRESSION PROFILING; GENETIC MARKER; GENETICS; MARKOV CHAIN; MOUSE; NUCLEOTIDE SEQUENCE; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS;

RNA;

ALGORITHMS; ANIMALS; BASE SEQUENCE; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENETIC MARKERS; MICE; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; STOCHASTIC PROCESSES;

EID: 85061399718     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/s41592-018-0308-4     Document Type: Article

References (26)

1. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Nat. Protoc. 13, 599–604 (2018).
2. 10X Genomics. Transciptional profiling of 1.3 million brain cells with the chromium single cell 3′ solution. SequMed BioTechnology http://www.sequmed.com/Private/Files/20170726/6363668905396462451645665.pdf (2017).
3. Tasic, B. et al. Nature 563, 72–78 (2018).
4. van der Maaten, L. J. Mach. Learn. Res. 15, 3221–3245 (2014).
5. Yianilos, P. N. in Proc. Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ‘93) 311–321 (Society for Industrial and Applied Mathematics, 1993).
6. Bernhardsson, E. Annoy: approximate nearest neighbors in C++/Python optimized for memory usage and loading/saving to disk. GitHub https://github.com/spotify/annoy (2017).
7. Linderman, G. C. & Steinerberger, S. arXiv Preprint at http://arXiv.org/1706.02582 (2017).
8. Belkina, A. C. et al. bioRxiv Preprint at https://www.biorxiv.org/content/early/2018/10/24/451690 (2018).
9. Kobak, D. & Berens, P. bioRxiv Preprint at https://www.biorxiv.org/content/early/2018/10/25/453449 (2018).
10. Cheng, Y., Wong, M. T., van der Maaten, L. & Newell, E. W. J. Immunol. 196, 924–932 (2015).
11. Galili, T., O’Callaghan, A., Sidi, J. & Sievert, C. Bioinformatics 34, 1600–1602 (2017).
12. Shekhar, K. et al. Cell 166, 1308–1323 (2016).
13. van der Maaten, L. & Hinton, G. J. Mach. Learn. Res. 9, 2579–2605 (2008).
14. Barnes, J. & Hut, P. Nature 324, 446–449 (1986).
15. Dahlquist, G. & Björck, Å. Numerical Methods in Scientific Computing Vol. 1 (Society for Industrial and Applied Mathematics, Philadelphia, 2008).
16. Trefethen, L. N. Approximation Theory and Approximation Practice (SIAM, Philadelphia, 2013).
17. Abramowitz, M. & Stegun, I. A. in Handbook of Mathematical Function: With Formulas, Graphs and Mathematical Tables (Dover Publications, Mineola, 1965).
18. Trefethen, L. N. & Weideman, J. A. C. J. Approx. Theory 65, 247–260 (1991).
19. Halko, N., Martinsson, P.-G., Shkolnisky, Y. & Tygert, M. SIAM J. Sci. Comput. 33, 2580–2594 (2011).
20. Halko, N., Martinsson, P.-G. & Tropp, J. A. SIAM Rev. 53, 217–288 (2011).
21. Witten, R. & Candes, E. Algorithmica 72, 264–281 (2015).
22. Li, H. et al. ACM Trans. Math. Software 43, 28 (2017).
23. Zheng, G. X. Y. et al. Nat. Commun. 8, 14049 (2017).
24. Wolf, F. A., Angerer, P. & Theis, F. J. Genome. Biol. 19, 15 (2018).
25. Hrvatin, S. et al. Nat. Neurosci. 21, 120–129 (2018).
26. Erichson, N. B., Voronin, S., Brunton, S. L. & Kutz, J. N. arXiv Preprint at http://arXiv.org/1608.02148 (2016).