Abstract
The development of second-generation sequencing technology has brought a great breakthrough to the study of biology. Clustering transcriptomes profiled by single-cell Ribonucleic Acid sequencing (scRNA-seq) has been routinely conducted to reveal cell heterogeneity and diversity. However, clustering analysis of scRNA-seq data remains a statistical and computational challenge, due to the pervasive drop-out events obscuring the data matrix with prevailing “false” zero count observations. In this paper, we propose a novel clustering technique named Deep Denoising Subspace Single-cell Clustering (DDS2C) to improve the clustering performance of scRNA-seq data, by utilizing autoencoder and data self-expressiveness structures. The DDS2C incorporates the loss functions of network structures and data denoising in a unified manner. The validity of DDS2C is examined over benchmark datasets from four representative single-cell sequencing platforms. The experimental results demonstrate that DDS2C outperforms than some state-of-the-art scRNA-seq clustering methods in terms of accuracy efficiency and scalability.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (NSFC) Grant (61806159); China Postdoctoral Science Foundation Grant (2018M631192) and Xi’an Municipal Science and Technology Program (2020KJRC0027).
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Wang, Y., Yang, B. (2020). Deep Denoising Subspace Single-Cell Clustering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_36
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DOI: https://doi.org/10.1007/978-3-030-63823-8_36
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