Abstract
As single-cell RNA sequencing (scRNA-seq) technology has rapidly become a powerful technique for revealing gene expression information at the cellular level. In scRNA-seq data analysis, cell clustering is a key step in downstream analysis as it can identify cell types and discover new cell subtypes. However, the high dimensionality, sparsity, and high noise characteristics of scRNA-seq datasets present significant challenges for clustering analysis.In this study, a model based on bipartite graph integration clustering and graph attention autoencoder is proposed. Firstly, the scRNA-seq dataset is preprocessed using network enhancement (NE) and principal component analysis (PCA) for denoising and feature selection. Next, a graph attention autoencoder is employed for dimension reduction to obtain low-dimensional embeddings. Finally, bipartite graph integration clustering is utilized to derive the final clustering results based on the relationship between cells and low-dimensional embeddings. Based on various clustering metrics, a comparison was made between different scRNA-seq datasets, and the experimental results showed that scBAGA outperformed other advanced methods. This indicates that our model can serve as a reliable classification tool.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62002189), the Natural Science Foundation of Shandong Province, China (No. ZR2020QF038), the Ability Improvement Project of Science and Technology SMES in Shandong Province (2023TSGC0279), the Youth Innovation Team of Colleges and Universities in Shandong Province (2023KJ329) and the Qilu University of Technology (Shandong Academy of Sciences) Talent Scientific Research Project (No. 2023RCKY128).
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Yuan, L. et al. (2024). Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with Graph Attention Autoencoders and Ensemble Clustering. In: Huang, DS., Pan, Y., Zhang, Q. (eds) Advanced Intelligent Computing in Bioinformatics. ICIC 2024. Lecture Notes in Computer Science(), vol 14882. Springer, Singapore. https://doi.org/10.1007/978-981-97-5692-6_6
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