Abstract
Over the past decade, high-throughput sequencing technologies have driven a dramatic increase in single-cell RNA sequencing (scRNA-seq) data. The study of scRNA-seq data has widened the scope and depth of researchers’ understanding of cellular heterogeneity. A prerequisite for studying heterogeneous cell populations is accurate cell type identification. However, the highly noisy and high-dimensional nature of scRNA-seq data poses a challenge to existing methods to further improve the success rate of cell type identification. Principal component analysis (PCA) is an important data analysis technique that is widely used to identify cell subpopulations. On the basis of PCA, we propose correntropy-based hypergraph regularized sparse PCA (CHLPCA) for accurate cell type identification. In addition to using correntropy to reduce the effect of noise, CHLPCA also considers higher-order relationships between samples by constructing the hypergraph, which compensates for the lack of local structure capture ability of PCA. Furthermore, we introduce the L2,1/5-norm into the model to enhance the interpretability of principal components (PCs), which further improves the model performance. CHLPCA has superior clustering accuracy and outperforms the best comparative method by 5.13% and 8.00% for ACC and NMI metrics, respectively. The results of clustering visualization experiments also confirm that CHLPCA can better perform the cell type recognition task.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62172253).
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Wang, TG., Kong, XZ., Li, SJ., Wang, J. (2023). CHLPCA: Correntropy-Based Hypergraph Regularized Sparse PCA for Single-Cell Type Identification. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_44
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DOI: https://doi.org/10.1007/978-981-99-7074-2_44
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