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A Deep Learning-Based Method Facilitates scRNA-seq Cell Type Identification

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Neural Computing for Advanced Applications (NCAA 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2181))

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Abstract

The repetitive analysis of similar scRNA-seq samples for cell identification is both time-consuming and labor-intensive. Transferring annotations from a reference to a query dataset offers innovative strategies for single-cell annotation, with deep learning’s prowess in feature extraction serving as a robust foundation for these approaches. In this paper, we present the Multi-level Feature Extractor-attention model (MLFE-Att), a deep learning framework that leverages the multi-head attention mechanism to learn intricate cellular correlation features, thereby enhancing the identification of cell categories with minor differences. Deeper and non-linear features of cells are learned by the module that contains multiple fully connected layers and activation layers. Experimental evaluations on the bone marrow scRNA-seq dataset demonstrate that MLFE-Att outperforms the other two baseline models, MLFE-CNN and MLFE-LSTM, with a 99.84% identification accuracy. The results on the new subset of the bone marrow dataset indicate that MLFE-Att offers dependable support for identifying cells in the new sample, achieving a 92.76% accuracy, and supplies insightful feedback for manual cellular labeling.

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Data Availability Statement

The dataset used in this study is obtained from public data repository and downloaded from the GEO database under the accession number GSE145477.

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Correspondence to Lin Meng .

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Wang, X., Li, Z., Han, J., Xu, R., Meng, L. (2025). A Deep Learning-Based Method Facilitates scRNA-seq Cell Type Identification. In: Zhang, H., Li, X., Hao, T., Meng, W., Wu, Z., He, Q. (eds) Neural Computing for Advanced Applications. NCAA 2024. Communications in Computer and Information Science, vol 2181. Springer, Singapore. https://doi.org/10.1007/978-981-97-7001-4_13

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  • DOI: https://doi.org/10.1007/978-981-97-7001-4_13

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  • Online ISBN: 978-981-97-7001-4

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