An improved multiomics data clustering algorithm based on graph regularization constraints

Shunqin Zhang; Wei Kong

doi:10.1117/12.3036749

13 July 2024 An improved multiomics data clustering algorithm based on graph regularization constraints

Shunqin Zhang, Wei Kong

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132081S (2024) https://doi.org/10.1117/12.3036749
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

Accurate clustering of cell type classification by integrating multi-omics data is necessary for subsequent data analysis. However, related algorithms face difficulties in dealing with noise and missing values present in multi-omics data. The network-based integrative clustering algorithm (NIC) can overcome the problem of data heterogeneity but does not fully utilize the spatial structure information in the data. To solve this problem, this study proposes a new algorithm-graph regularization-based NIC (GR-NIC) to improve the integration accuracy, remove redundant features, and preserve the geometric structure of the data by fusing single-cell multi-omics data, single-cell sequencing data (scRNA-seq or scATACseq) and histone modification data. To verify the effectiveness of the GR-NIC algorithm, this paper compares three state-of-the-art multi-omics clustering algorithms through five datasets. The results show that GR-NIC can select features that are more representative of the structure of the data and improve the accuracy of clustering. This new approach is effectively validated on mouse brain and peripheral blood sample datasets and shows significant improvements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shunqin Zhang and Wei Kong "An improved multiomics data clustering algorithm based on graph regularization constraints", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132081S (13 July 2024); https://doi.org/10.1117/12.3036749

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