Issue |
BIO Web Conf.
Volume 59, 2023
2023 5th International Conference on Biotechnology and Biomedicine (ICBB 2023)
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Article Number | 03004 | |
Number of page(s) | 6 | |
Section | Clinical Trials and Medical Device Monitoring | |
DOI | https://doi.org/10.1051/bioconf/20235903004 | |
Published online | 08 May 2023 |
Application of machine learning to associative scRNA-seq data gene expression and alternative polyadenylation sites clustering
1 University of South China, Hengyang Hunan 421001, China
2 Institute of Health Service and Transfusion Medicine, Beijing, 100850, China
3 Beijing Institute of Microbiology and Epidemiology, Beijing, 100850, China
4 Department of Genetics & Integrative Omics, State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing, 100850, P. R. China
* E-mail: zhougq114@126.com
Cell type identification is a vital step in the analysis of scRNA-seq data. Transcriptome subtype pivotal information such as alternative polyadenylation (APA) obtained from standard scRNA-seq data can also provide valid clues for cell type identification with no alteration of experimental techniques or increased experimental costs. Furthermore, using multimodal analysis techniques and their methods, more confident cell type identification results can be obtained. For that purpose, we constructed a workflow framework: On five different scRNA-seq datasets, 18 methods based on machine learning that have not yet been applied to identify cell types by association APA and single-cell gene expression fusion were compared with three single-cell clustering methods, and compared these method against the advanced method scLAPA based on similarity network fusion (SNF). In our experiments, we used the adjusted Rand index (ARI) as a metric. We found that unsupervised methods like WMSC and supervised methods like MOGONET have more robust and excellent results in associating APA with single-cell gene expression clustering than methods based only on single-cell gene expression clustering and advanced scLAPA methods.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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