Abstract
Identifying cell types is one of the most important goals in single cell sequencing data analysis. The similarity between cells is the principal basis for dividing cell subpopulations. However, it usually brings higher computational complexity when using multi kernel learning. In this paper, a new method SHDC was proposed for single cell clustering. SHDC denoised scRNA-seq datasets to obtain a more stable data structure and only a heat kernel was used to measure the similarity between cells. We used several real scRNA-seq datasets to test the performance of SHDC and compared with previous single cell clustering methods. The results show that our method is effective.
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Acknowledgements
This work was supported by grants from the Xinjiang Autonomous Region University Research Program (No. XJEDU2019Y002) and the National Natural Science Foundation of China (No. U19A2064, 61873001).
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Zhao, Jp., Wang, Hy., Zheng, CH. (2021). SHDC: A Method of Similarity Measurement Using Heat Kernel Based on Denoising for Clustering scRNA-seq Data. 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_29
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DOI: https://doi.org/10.1007/978-3-030-84532-2_29
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