Abstract
In recent years, with the development of single-cell RNA sequencing (scRNA-seq) technology, more and more scRNA-seq data has been generated. Corresponding analysis methods such as clustering analysis are also proposed, which effectively distinguish the cell types and reveal the cell diversity. However, due to more than ten thousand genes for normal species, the dimension of scRNA-seq data is very high. Meanwhile, there exist many zero counts in scRNA-seq data. They all increase the difficulty of clustering analysis of scRNA-seq data. This paper proposes ScSSC, a semi-supervised clustering method based on 2D embedding. ScSSC uses the autoencoder for pre-training to construct the network and applies the community discovery algorithm to label cells. Then a semi-supervised network is used to clustering the data after training. The clustering results of three public data sets show that ScSSC has better performance than other clustering methods.
N. Shi and Y. Wu—Contributed equally to this work.
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Acknowledgements
This work was supported by the grants from the National Key Research Program (2017YFC1201201, 2018YFC0910504 and 2017YFC0907503), Shenzhen Science and Technology Program the university stable support program (20200821222112001) from JL.
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NS, YW and JL designed the study, performed bioinformatics analysis and drafted the manuscript. All of the authors performed the analysis and participated in the revision of the manuscript. JL conceived of the study, participated in its design and coordination and drafted the manuscript. All authors read and approved the final manuscript.
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Shi, N., Wu, Y., Du, L., Liu, B., Wang, Y., Li, J. (2021). ScSSC: Semi-supervised Single Cell Clustering Based on 2D Embedding. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_43
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