Shapiro E, Biezuner T, Linnarsson S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet. 2013;14(9):618–30.
CAS
PubMed
Google Scholar
Wang Y, Navin NE. Advances and applications of single-cell sequencing technologies. Mol Cell. 2015;58(4):598–609.
CAS
PubMed
PubMed Central
Google Scholar
Eberwine J, Sul J-Y, Bartfai T, Kim J. The promise of single-cell sequencing. Nat Methods. 2014;11(1):25–7.
CAS
PubMed
Google Scholar
Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 2019;10(1):390.
CAS
PubMed
PubMed Central
Google Scholar
Haghverdi L, Lun AT, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36(5):421–7.
CAS
PubMed
PubMed Central
Google Scholar
Aibar S, González-Blas CB, Moerman T, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, van den Oord J, et al. Scenic: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083–6.
CAS
PubMed
PubMed Central
Google Scholar
Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods. 2017;14(4):414–6.
CAS
PubMed
Google Scholar
Villani A-C, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science. 2017;356(6335):4573.
Google Scholar
Kester L, van Oudenaarden A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell. 2018;23(2):166–79.
CAS
PubMed
Google Scholar
Biase F, Wu Q, Calandrelli R, Rivas-Astroza M, Zhou S, Chen Z, Zhong S. Rainbow-seq: combining cell lineage tracking with single-cell RNA sequencing in preimplantation embryos. iScience. 2018;7:16–29.
CAS
PubMed
PubMed Central
Google Scholar
Chen H, Albergante L, Hsu JY, Lareau CA, Bosco GL, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan G-C, Pinello L. Single-cell trajectories reconstruction, exploration and mapping of omics data with stream. Nat Commun. 2019;10(1):1903.
PubMed
PubMed Central
Google Scholar
Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth B, et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell. 2017;169(7):1276–90.
CAS
PubMed
Google Scholar
Kim K-T, Lee HW, Lee H-O, Kim SC, Seo YJ, Chung W, Eum HH, Nam D-H, Kim J, Joo KM, et al. Single-cell MRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015;16(1):127.
PubMed
PubMed Central
Google Scholar
Clarke MF, Quake SR, Dalerba PD, Liu H, Leyrat A, Kalisky T, Diehn M, Wang J. Single cell gene expression for diagnosis, prognosis and identification of drug targets. Google Patents. US Patent 9,329,170 (2016)
Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, Rothenberg ME, Mburu FM, Mantalas GL, Sim S, Clarke MF, et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods. 2014;11(1):41–6.
CAS
PubMed
Google Scholar
Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014;11(7):740–2.
CAS
PubMed
PubMed Central
Google Scholar
Hicks SC, Townes FW, Teng M, Irizarry RA. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics. 2017;19(4):562–78.
PubMed Central
Google Scholar
Li R, Guan J, Zhou S. Single-cell RNA-seq data clustering: a survey with performance comparison study. J Bioinform Comput Biol. 2020;18(4):2040005.
CAS
PubMed
Google Scholar
Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, et al. Sc3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14(5):483–6.
CAS
PubMed
PubMed Central
Google Scholar
Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. Sincera: a pipeline for single-cell RNA-seq profiling analysis. PLoS Comput Biol. 2015;11(11):1004575.
Google Scholar
Lin P, Troup M, Ho JW. Cidr: Ultrafast and accurate clustering through imputation for single-cell rna-seq data. Genome Biol. 2017;18(1):59.
PubMed
PubMed Central
Google Scholar
Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A. Single-cell messenger rna sequencing reveals rare intestinal cell types. Nature. 2015;525(7568):251–5.
PubMed
Google Scholar
Yau C, et al. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinform. 2016;17(1):140.
Google Scholar
Ester M, Kriegel H-P, Sander J, Xu X, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996;96(34):226–31.
Zhou S, Zhou A, Jin W, Fan Y, Qian W. Fdbscan: a fast dbscan algorithm. Ruan Jian Xue Bao. 2000;11(6):735–44.
Google Scholar
Jiang L, Chen H, Pinello L, Yuan G-C. Giniclust: detecting rare cell types from single-cell gene expression data with gini index. Genome Biol. 2016;17(1):144.
PubMed
PubMed Central
Google Scholar
Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics. 2015;31(12):1974–80.
CAS
PubMed
PubMed Central
Google Scholar
Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502.
CAS
PubMed
PubMed Central
Google Scholar
Sun Z, Wang T, Deng K, Wang X-F, Lafyatis R, Ding Y, Hu M, Chen W. DIMM-SC: a dirichlet mixture model for clustering droplet-based single cell transcriptomic data. Bioinformatics. 2017;34(1):139–46.
PubMed Central
Google Scholar
Prabhakaran S, Azizi E, Carr A, Pe’er D. Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. Int Conf Mach Learn. 2016;48:1070–9.
Google Scholar
Shao C, Höfer T. Robust classification of single-cell transcriptome data by nonnegative matrix factorization. Bioinformatics. 2017;33(2):235–42.
CAS
PubMed
Google Scholar
Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, Marques S, Munguba H, He L, Betsholtz C, et al. Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq. Science. 2015;347(6226):1138–42.
CAS
PubMed
Google Scholar
Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053–8.
CAS
PubMed
PubMed Central
Google Scholar
Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lönnerberg P, Linnarsson S. Quantitative single-cell rna-seq with unique molecular identifiers. Nat Methods. 2014;11(2):163–6.
CAS
PubMed
Google Scholar
Picelli S, Bjrklund SK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–8.
CAS
PubMed
Google Scholar
Han X, Wang R, Zhou Y, Fei L, Guo G. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;172(5):1307.
Google Scholar
Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):1–12.
Google Scholar
Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2(1):193–218.
Google Scholar
Davies DL, Bouldin DW. A cluster separation measure. IEEE Trans Pattern Anal Intell. 1979;PAMI–1(2):224–7.
Google Scholar
Arthur D, Vassilvitskii S. k-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp. 1027–1035 (2007). Society for Industrial and Applied Mathematics
Reynolds AP, Richards G, Iglesia BDL, Rayward-Smith VJ. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J Math Model Algorithms. 2006;5(4):475–504.
Google Scholar
Johnson SC. Hierarchical clustering schemes. Psychometrika. 1967;32(3):241–54.
CAS
PubMed
Google Scholar
Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016;8(1):289–317.
PubMed
PubMed Central
Google Scholar
Duò A, Robinson MD, Soneson C. A systematic performance evaluation of clustering methods for single-cell rna-seq data. F1000Research. 2018;7:1141.
PubMed
Google Scholar
Andrews TS, Hemberg M. Identifying cell populations with scrnaseq. Mol Aspects Med. 2018;59:114–22.
CAS
PubMed
Google Scholar
Maaten, L.v.d., Hinton, G. Visualizing data using t-sne. J Mach Learn Res. 2008;9:2579–605.
Becht E, Mcinnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using umap. Nat Biotechnol. 2019;37:38–44.
CAS
Google Scholar
Frigui H, Nasraoui O. Simultaneous categorization of text documents and identification of cluster-dependent keywords. In: 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE’02. Proceedings (Cat. No. 02CH37291), vol. 2, pp. 1108–1113 (2002). IEEE
Liao R, Zhang R, Guan J, Zhou S. A new unsupervised binning approach for metagenomic sequences based on n-grams and automatic feature weighting. IEEE/ACM Trans Comput Biol Bioinf. 2013;11(1):42–54.
Google Scholar
Wan L, Ding J, Jin T, Guan J, Zhou S. Automatically clustering large-scale miRNA sequences: methods and experiments. BMC Genom. 2012;13(S8):15.
Google Scholar
Harpeled S, Mazumdar S. Coresets for k-means and k-median clustering and their applications. In: Annual of ACM Symposium on Theory of Computing, 2004;291–300.
Sturges HA. The choice of a class interval. J Am Stat Assoc. 1926;21(153):65–6.
Google Scholar
Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science. 2014;344(6191):1492–6.
CAS
PubMed
Google Scholar
Biase FH, Cao X, Zhong S. Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell rna sequencing. Genome Res. 2014;24(11):1787–96.
CAS
PubMed
PubMed Central
Google Scholar
Ramskold D, Luo S, Wang Y, Li R, Deng Q, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, et al. Full-length mrna-seq from single-cell levels of rna and individual circulating tumor cells. Nat Biotechnol. 2012;30(8):777–82.
PubMed
PubMed Central
Google Scholar
Yan L, Yang M, Guo H, Yang L, Wu J, Li R, Liu P, Lian Y, Zheng X, Yan J, et al. Single-cell rna-seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. 2013;20(9):1131–9.
CAS
PubMed
Google Scholar
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A. mrna-seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–82.
CAS
PubMed
Google Scholar
Goolam M, Scialdone A, Graham SJ, Macaulay IC, Jedrusik A, Hupalowska A, Voet T, Marioni JC, Zernicka-Goetz M. Heterogeneity in oct4 and sox2 targets biases cell fate in 4-cell mouse embryos. Cell. 2016;165(1):61–74.
CAS
PubMed
PubMed Central
Google Scholar
Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell rna-seq. Nature. 2014;509(7500):371–5.
CAS
PubMed
PubMed Central
Google Scholar
Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, Li N, Szpankowski L, Fowler B, Chen P, et al. Low-coverage single-cell mrna sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32(10):1053–8.
CAS
PubMed
PubMed Central
Google Scholar