组学
聚类分析
计算机科学
鉴定(生物学)
亚型
子空间拓扑
生物标志物发现
数据挖掘
高维数据聚类
生物信息学
计算生物学
机器学习
蛋白质组学
人工智能
生物
生物化学
植物
基因
程序设计语言
作者
Xiucai Ye,Yifan Shang,Tianyi Shi,Weihang Zhang,Tetsuya Sakurai
标识
DOI:10.1016/j.compbiomed.2023.107223
摘要
The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.
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