Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

Ai, S.S., Xiong, H.Q., Li, C.C., Luo, Y.J., Shi, Q., Liu, Y.X., et al. (2019). Profiling chromatin states using single cell itChIP-seq. Nature Cell Biology, 21(9), 1164–1172. AiS.S. XiongH.Q. LiC.C. LuoY.J. ShiQ. LiuY.X. 2019 Profiling chromatin states using single cell itChIP-seq Nature Cell Biology 21 9 1164 1172 10.1038/s41556-019-0383-531481796 Search in Google Scholar

Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4/5), 993–1022. BleiD.M. NgA.Y. JordanM.I. 2003 Latent Dirichlet allocation Journal of Machine Learning Research 3 4/5 993 1022 Search in Google Scholar

Briggs, J.A., Weinreb, C., Wagner, D.E., Megason, S., Peshkin, L., Kirschner, M.W., et al. (2018). The dynamics of gene expression in vertebrate embryogenesis at single cell resolution. Science, 360(6392). pii: eaar5780. BriggsJ.A. WeinrebC. WagnerD.E. MegasonS. PeshkinL. KirschnerM.W. 2018 The dynamics of gene expression in vertebrate embryogenesis at single cell resolution Science 360 6392 pii: eaar5780. 10.1126/science.aar5780603814429700227 Search in Google Scholar

Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D.M., Hill, A.J., et al. (2019). The single cell transcriptional landscape of mammalian organogenesis. Nature, 566(7745), 496–502. CaoJ. SpielmannM. QiuX. HuangX. IbrahimD.M. HillA.J. 2019 The single cell transcriptional landscape of mammalian organogenesis Nature 566 7745 496 502 10.1038/s41586-019-0969-x643495230787437 Search in Google Scholar

Farrell, J.A., Wang, Y., Riesenfeld, S.J., Shekhar, K., Regev, A., & Schier, A.F. (2018). Single cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science, 360(6392). pii: eaar3131. FarrellJ.A. WangY. RiesenfeldS.J. ShekharK. RegevA. SchierA.F. 2018 Single cell reconstruction of developmental trajectories during zebrafish embryogenesis Science 360 6392 pii: eaar3131. 10.1126/science.aar3131624791629700225 Search in Google Scholar

Griffiths, J.A., Scialdone, A., & Marioni, J.C. (2018). Using single cell genomics to understand developmental processes and cell fate decisions. Molecular System Biology, 14(4), e8046. GriffithsJ.A. ScialdoneA. MarioniJ.C. 2018 Using single cell genomics to understand developmental processes and cell fate decisions Molecular System Biology 14 4 e8046 10.15252/msb.20178046590044629661792 Search in Google Scholar

Guo, X.Y., Zhang, Y.Y., Zheng, L.T., Zheng, C.H., Song, J.T., Zhang, Q.M., et al. (2018). Global characterization of T cells in non-small-cell lung cancer by single cell sequencing. Nature Medicine, 24(7), 978–985. GuoX.Y. ZhangY.Y. ZhengL.T. ZhengC.H. SongJ.T. ZhangQ.M. 2018 Global characterization of T cells in non-small-cell lung cancer by single cell sequencing Nature Medicine 24 7 978 985 10.1038/s41591-018-0045-329942094 Search in Google Scholar

Jelodar, H., Wang, Y.L., Yuan, C., Feng, X., Jiang, X.H., Li, Y.C., et al. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169–15211. JelodarH. WangY.L. YuanC. FengX. JiangX.H. LiY.C. 2019 Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey Multimedia Tools and Applications 78 11 15169 15211 10.1007/s11042-018-6894-4 Search in Google Scholar

Junker, J.P., & van Oudenaarden A. (2014). Every cell is special: Genome-wide studies add a new dimension to single cell biology. Cell, 157(1), 8–11. JunkerJ.P. van OudenaardenA. 2014 Every cell is special: Genome-wide studies add a new dimension to single cell biology Cell 157 1 8 11 10.1016/j.cell.2014.02.01024679522 Search in Google Scholar

Lee, L. (2001). On the Eectiveness of the skew divergence for statistical language analysis [C]//Richardson T.S, Jaakkola T.S. In the Proceedings of the English International Workshop on Artificial Intelligence and Statistics. Key West: Society for Artificial Intelligence and Statistics. 65–72. LeeL. 2001 On the Eectiveness of the skew divergence for statistical language analysis [C] RichardsonT.S JaakkolaT.S. In the Proceedings of the English International Workshop on Artificial Intelligence and Statistics Key West: Society for Artificial Intelligence and Statistics 65 72 Search in Google Scholar

Lindström, S. (2012). Flow Cytometry and Microscopy as Means of Studying Single Cells: A Short Introductional Overview. In: Lindström, S., & Andersson-Svahn, H. (Ed.), Single cell Analysis. Methods in Molecular Biology (Methods and Protocols), 853 (pp.13–15). Humana Press. LindströmS. 2012 Flow Cytometry and Microscopy as Means of Studying Single Cells: A Short Introductional Overview In: LindströmS. Andersson-SvahnH. (Ed.), Single cell Analysis. Methods in Molecular Biology (Methods and Protocols) 853 13 15 Humana Press 10.1007/978-1-61779-567-1_222323136 Search in Google Scholar

Nicolaisen, J. (2010). Bibliometrics and citation analysis: From the science citation index to cybermetrics. Journal of the American Society for Information Science and Technology, 61(1), 205–207. NicolaisenJ. 2010 Bibliometrics and citation analysis: From the science citation index to cybermetrics Journal of the American Society for Information Science and Technology 61 1 205 207 10.1002/asi.21181 Search in Google Scholar

Reece, A., Xia, B.Z., Jiang, Z.L., Noren, B., McBride, R., & Oakey, J. (2016). Microfluidic techniques for high throughput single cell analysis. Current Opinion in Biotechnology, 40, 90–96. ReeceA. XiaB.Z. JiangZ.L. NorenB. McBrideR. OakeyJ. 2016 Microfluidic techniques for high throughput single cell analysis Current Opinion in Biotechnology 40 90 96 10.1016/j.copbio.2016.02.015497561527032065 Search in Google Scholar

Wang, Q.H., Xiong H.Q., Ai, S.S., Yu, X.H., Liu, Y.X., Zhang, J.J., et al. (2019) CoBATCH for High-Throughput Single cell Epigenomic Profiling. Molecular Cell, 76(1), 206–216.e7. WangQ.H. XiongH.Q. AiS.S. YuX.H. LiuY.X. ZhangJ.J. 2019 CoBATCH for High-Throughput Single cell Epigenomic Profiling Molecular Cell 76 1 206 216.e7 10.1016/j.molcel.2019.07.01531471188 Search in Google Scholar

Wei, W., Shin, Y.S., Xue, M., Matsutani, T., Masui, K., Yang, H., et al. (2016). Single cell Phosphoproteomics Resolves Adaptive Signaling Dynamics and Informs Targeted Combination Therapy in Glioblastoma. Cancer Cell, 29(4), 563–573. WeiW. ShinY.S. XueM. MatsutaniT. MasuiK. YangH. 2016 Single cell Phosphoproteomics Resolves Adaptive Signaling Dynamics and Informs Targeted Combination Therapy in Glioblastoma Cancer Cell 29 4 563 573 10.1016/j.ccell.2016.03.012483107127070703 Search in Google Scholar

Zhang, L., Yu, X., Zheng, L.T., Zhang, Y.Y., Li, Y.S., Fang, Q., et al. (2018). Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature, 564, 268–272. ZhangL. YuX. ZhengL.T. ZhangY.Y. LiY.S. FangQ. 2018 Lineage tracking reveals dynamic relationships of T cells in colorectal cancer Nature 564 268 272 10.1038/s41586-018-0694-x30479382 Search in Google Scholar

Zhang, W., Xu, X.L., Ming, C.H., Mao, Z.J., Shi, J., & Xiang, Y.Q. (2016). Surviving in the dispute: A bibliometric analysis of global GMF-related research, 1995–2014. Scientometrics, 109(1), 359–375. ZhangW. XuX.L. MingC.H. MaoZ.J. ShiJ. XiangY.Q. 2016 Surviving in the dispute: A bibliometric analysis of global GMF-related research, 1995–2014 Scientometrics 109 1 359 375 10.1007/s11192-016-1995-1 Search in Google Scholar

Zheng, T.L., Wang, J., Wang, Q.H., Nie, C.H., Shi, Z.N., Wang, X.N., et al. (2016). A bibliometric analysis of micro/nano-bubble related research: Current trends, present application, and future prospects. Scientometrics, 109(1), 53–71. ZhengT.L. WangJ. WangQ.H. NieC.H. ShiZ.N. WangX.N. 2016 A bibliometric analysis of micro/nano-bubble related research: Current trends, present application, and future prospects Scientometrics 109 1 53 71 10.1007/s11192-016-2004-4 Search in Google Scholar