Abstract
Single-cell RNA sequencing (scRNA-seq) data serves as the foundation for many studies investigating cellular heterogeneity. Numerous methodologies and evaluation metrics within single-cell research are intertwined with cell labels. While annotating cell labels often requires prior biological knowledge for clustering, this is frequently approached from a clustering perspective rather than considering the heterogeneity of individual cells. Building upon this, we introduce a data-driven self-correction framework for labeled scRNA-seq data, termed scCoRR. This framework utilizes a supervised approach trained from partially reliable anchor cells, eliminating the need for additional prior reference datasets or marker genes. Subsequently, a supervised deep neural network is trained with cross-entropy loss and a contrastive regularization term to predict the types of the remaining cells. During this process, the labels of some cells are corrected from one cell type to another, a phenomenon that can also be elucidated from various biological perspectives.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 62202503, 62225209, Hunan Provincial Natural Science Foundation of China under Grant No. 2023JJ40780.
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He, Y., Liu, J., Li, M., Zheng, R. (2024). scCoRR: A Data-Driven Self-correction Framework for Labeled scRNA-Seq Data. In: Peng, W., Cai, Z., Skums, P. (eds) Bioinformatics Research and Applications. ISBRA 2024. Lecture Notes in Computer Science(), vol 14955. Springer, Singapore. https://doi.org/10.1007/978-981-97-5131-0_5
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DOI: https://doi.org/10.1007/978-981-97-5131-0_5
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