Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data

Cell Systems

Volumn 8, Issue 4, 2019, Pages 281-291.e9

  Wolock, Samuel L ^a   Lopez, Romain ^a,b   Klein, Allon M ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

^b CENTRE DE MATHÉMATIQUES APPLIQUÉES   (France)

Author keywords

artifact detection; bioinformatics; cell doublets; decoy classifier; high dimensional data analysis; RNA seq; single cell

Indexed keywords

ARTICLE; ARTIFACT; BIOINFORMATICS; CLASSIFIER; DATA ANALYSIS; HUMAN; ANIMAL; MOUSE; PROCEDURES; SINGLE CELL ANALYSIS; SOFTWARE;

TRANSCRIPTOME;

ANIMALS; ARTIFACTS; HUMANS; MICE; RNA-SEQ; SINGLE-CELL ANALYSIS; SOFTWARE; TRANSCRIPTOME;

EID: 85064408983     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2018.11.005     Document Type: Article

References (30)

1. Adamson, B., Norman, T.M., Jost, M., Cho, M.Y., Nunez, J.K., Chen, Y., Villalta, J.E., Gilbert, L.A., Horlbeck, M.A., Hein, M.Y., et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167 (2016), 1867–1882.e21.
2. Benjamini, Y., Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B (Methodol.) 57 (1995), 289–300.
3. Bernhardsson, E. (2013). Annoy: approximate nearest neighbors in C++/Python optimized for memory usage and loading/saving to disk (2013).
4. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp., 2008.
5. Cao, J., Packer, J.S., Ramani, V., Cusanovich, D.A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S.N., Steemers, F.J., et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357 (2017), 661–667.
6. Datlinger, P., Rendeiro, A.F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L.C., Kuchler, A., Alpar, D., Bock, C., Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14 (2017), 297–301.
7. Elias, J.E., Gygi, S.P., Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604 (2010), 55–71.
8. Gehring, J., Park, J.H., Chen, S., Thomson, M., Pachter, L., Highly multiplexed single-cell RNA-seq for defining cell population and transcriptional spaces. bioRxiv, 2018.
9. Gierahn, T.M., Wadsworth, M.H. 2nd, Hughes, T.K., Bryson, B.D., Butler, A., Satija, R., Fortune, S., Love, J.C., Shalek, A.K., Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14 (2017), 395–398.
10. Griffiths, J.A., Scialdone, A., Marioni, J.C., Using single-cell genomics to understand developmental processes and cell fate decisions. Mol. Syst. Biol., 14, 2018.
11. Grun, D., Muraro, M.J., Boisset, J.C., Wiebrands, K., Lyubimova, A., Dharmadhikari, G., van den Born, M., van Es, J., Jansen, E., Clevers, H., et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19 (2016), 266–277.
12. Guo, C., Kong, W., Kamimoto, K., Rivera-Gonzalez, G.C., Yang, X., Kirita, Y., Morris, S.A., CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics. bioRxiv, 2019, 10.1101/335547.
13. Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. Mapping the mouse cell atlas by microwell-seq. Cell 172 (2018), 1091–1107.e17.
14. Jaitin, D.A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343 (2014), 776–779.
15. Kang, H.M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C.M., et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36 (2018), 89–94.
16. Klein, A.M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D.A., Kirschner, M.W., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161 (2015), 1187–1201.
17. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
18. Manwani, D., Bieker, J.J., The erythroblastic island. Curr. Top. Dev. Biol. 82 (2008), 23–53.
19. McGinnis, C.S., Murrow, L.M., Gartner, Z.J., DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst., 8, 2019, 10.1016/j.cels.2019.03.003 Published online April 3, 2018.
20. McGinnis, C.S., Patterson, D.M., Winkler, J., Hein, M.Y., Srivastava, V., Conrad, D.N., Murrow, L.M., Weissman, J.S., Werb, Z., Chow, E.D., Gartner, Z.J., MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. bioRxiv., 2018, 10.1101/387241.
21. McInnes, L., Healy, J., Saul, N., Großberger, L., UMAP: uniform manifold approximation and projection. J. Open Source Softw., 3, 2018.
22. Picelli, S., Bjorklund, A.K., Faridani, O.R., Sagasser, S., Winberg, G., Sandberg, R., Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10 (2013), 1096–1098.
23. Rosenberg, A.B., Roco, C.M., Muscat, R.A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L.T., Peeler, D.J., Mukherjee, S., Chen, W., et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360 (2018), 176–182.
24. Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B.Z., Mauck, W.M. 3rd, Smibert, P., Satija, R., Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol., 19, 2018, 224.
25. Tusi, B.K., Wolock, S.L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J.R., Klein, A.M., Socolovsky, M., Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555 (2018), 54–60.
26. van der Maaten, L., Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15 (2014), 3221–3245.
27. Weinreb, C., Wolock, S., Klein, A.M., SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34 (2018), 1246–1248.
28. Xu, J., Falconer, C., Coin, L., Genotype-free demultiplexing of pooled single-cell RNA-seq. bioRxiv., 2019, 10.1101/570614.
29. Zappia, L., Phipson, B., Oshlack, A., Splatter: simulation of single-cell RNA sequencing data. Genome Biol., 18, 2017, 174.
30. Zheng, G.X., Terry, J.M., Belgrader, P., Ryvkin, P., Bent, Z.W., Wilson, R., Ziraldo, S.B., Wheeler, T.D., McDermott, G.P., Zhu, J., et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun., 8, 2017, 14049.