Abstract
With the development of single-cell RNA sequencing (scRNA-seq) technology, characterizing heterogeneity at the cellular level has become a new area of computational biology research. However, the infiltration of different types of cells and the high variability in gene expression complicate classification of cell types. In this study, we propose an improved spectral clustering method for clustering single-cell data that avoid the overfitting issue and consider both similarity and dissimilarity, motivated by the observation that same type cells have similar gene expression patterns, but different types of cells produce dissimilar gene expression patterns. To evaluate the performance of the proposed spectral clustering method, we compare it with the traditional spectral clustering method in recognizing cell types on various real scRNA-seq data. The results show that taking intercellular dissimilarity into account can effectively achieve high accuracy and robustness and that our method outperforms the traditional spectral clustering methods in grouping cells that belong to the same cell types.
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References
Liang, J., Cai, W., Sun, Z.: Single-cell sequencing technologies: current and future. J. Genet. Genom. 41(10), 513–528 (2014)
Shekhar, K., Lapan, S.W., Whitney, I.E., Tran, N.M., Macosko, E.Z., Kowalczyk, M., et al.: Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166(5), 1308–23.e30 (2016)
Chung, W., Eum, H.H., Lee, H.O., Lee, K.M., Lee, H.B., Kim, K.T., et al.: Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8, 15081 (2017)
Buettner, F., Natarajan, K.N., Casale, F.P., Proserpio, V., Scialdone, A., Theis, F.J., et al.: Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33(2), 155–160 (2015)
Xu, C., Su, Z.: Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31(12), 1974–1980 (2015)
Tian, L.-P., Luo, P., Wang, H., Zheng, H., Wu, F.-X.: CASNMF: a converged algorithm for symmetrical nonnegative matrix factorization. Neurocomputing 275, 2031–2040 (2018)
Li, L.X., Wu, L., Zhang, H.S., Wu, F.X.: A fast algorithm for nonnegative matrix factorization and its convergence. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1855–1863 (2014)
Shao, C., Höfer, T.: Robust classification of single-cell transcriptome data by nonnegative matrix factorization. Bioinformatics 33(2), 235–242 (2017)
Kiselev, V.Y., Kirschner, K., Schaub, M.T., Andrews, T., Yiu, A., Chandra, T., et al.: SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14(5), 483 (2017)
Kiselev, V.Y., Andrews, T.S., Hemberg, M.: Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 1 (2019)
Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Lu, C., Yan, S., Lin, Z.: Convex sparse spectral clustering: single-view to multi-view. IEEE Trans. Image Process. 25(6), 2833–2843 (2016)
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 14(4), 414–416 (2017)
Park, S., Zhao, H.: Spectral clustering based on learning similarity matrix. Bioinformatics 34(12), 2069–2076 (2018)
Ting, D.T., Wittner, B.S., Ligorio, M., Jordan, N.V., Shah, A.M., Miyamoto, D.T., et al.: Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 8(6), 1905–1918 (2014)
Treutlein, B., Brownfield, D.G., Wu, A.R., Neff, N.F., Mantalas, G.L., Espinoza, F.H., et al.: Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500), 371–375 (2014)
Deng, Q., Ramsköld, D., Reinius, B., Sandberg, R.: Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343(6167), 193–196 (2014)
Wu, F.X., Zhang, W.J., Kusalik, A.J.: Dynamic model-based clustering for time-course gene expression data. J. Bioinform. Comput. Biol. 3(4), 821–836 (2005)
Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3(11), 583–617 (2002)
Hamatani, T., Carter, M.G., Sharov, A.A., Ko, M.S.: Dynamics of global gene expression changes during mouse preimplantation development. Dev. Cell 6(1), 117–131 (2004)
Acknowledgments
This work was supported in part by Natural Science and Engineering Research Council of Canada (NSERC), China Scholarship Council (CSC), the National Natural Science Foundation of China under Grant No. 61571052 and by the Science Foundation of Wuhan Institute of Technology under Grant No. K201746.
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Li, Y., Luo, P., Lu, Y., Wu, FX. (2019). Improved Spectral Clustering Method for Identifying Cell Types from Single-Cell Data. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_17
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DOI: https://doi.org/10.1007/978-3-030-26969-2_17
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