References
- 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.
- Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4/5), 993–1022.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.