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13 July 2024 An accurate clustering algorithm for single-cell multiomics data
Hao Dong, Wei Kong, Wenliang Gao, Zhong Chen
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Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132081X (2024) https://doi.org/10.1117/12.3036648
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China
Abstract
The fusion of single-cell multi-omics data not only provides us with a more comprehensive and in-depth understanding of cellular states and biological processes, but also greatly facilitates the study of the full range of intracellular regulatory mechanisms. However, single-cell data usually contain much noise, making data analysis difficult. The Joint SemiOrthogonal Nonnegative Matrix Factorization (JSNMF) algorithm can effectively reduce the noise in different histological data from the same sample by applying the coherence graph fusion method to achieve the integration of two histological data. However, JSNMF takes a step of dimensionality reduction followed by clustering when dealing with single-cell data, which limits its ability to uncover unique patterns between different datasets. In this study, we propose an improved method, Joint Learning Dimension Reduction Semi-Orthogonal Nonnegative Matrix Factorization (JLDRSNMF), which mines unique patterns among different datasets by incorporating Joint Learning Dimension Reduction into the JSNMF algorithm to improve the accuracy of the model. Meanwhile, we applied JLDRSNMF to five multi-omics datasets and compared it with two other JSNMF algorithms. The results show that JLDRSNMF can effectively integrate the information of the same cell from different histological data and significantly improve the accuracy of cell labeling prediction, thus greatly facilitating the subsequent analysis.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Hao Dong, Wei Kong, Wenliang Gao, and Zhong Chen "An accurate clustering algorithm for single-cell multiomics data", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132081X (13 July 2024); https://doi.org/10.1117/12.3036648
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KEYWORDS
Matrices
Data modeling
Data integration
Data fusion
Dimension reduction
Machine learning
Mathematical modeling
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