Comprehensive Evaluation of Multi-Omics Clustering Algorithms for Cancer Molecular Subtyping

• Published in: International journal of molecular sciences Vol. 26; no. 3; p. 963
• Main Authors: Wang, Juan, Wang, Lingxiao, Liu, Yi, Li, Xiao, Ma, Jie, Li, Mansheng, Zhu, Yunping
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2025; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Breast cancer; Cancer; Cluster Analysis; Clustering; Clustering Algorithms; Computational Biology - methods; Datasets; Deep learning; Gene Expression Profiling; Genomics - methods; Humans; Multiomics; Neoplasms - classification; Neoplasms - genetics; Neoplasms - metabolism; Survival analysis; multi-omics; clustering algorithm; accuracy-weighted average index; cancer subtyping
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms26030963

As a highly heterogeneous and complex disease, the identification of cancer’s molecular subtypes is crucial for accurate diagnosis and personalized treatment. The integration of multi-omics data enables a comprehensive interpretation of the molecular characteristics of cancer at various biological levels. In recent years, an increasing number of multi-omics clustering algorithms for cancer molecular subtyping have been proposed. However, the absence of a definitive gold standard makes it challenging to evaluate and compare these methods effectively. In this study, we developed a general framework for the comprehensive evaluation of multi-omics clustering algorithms and introduced an innovative metric, the accuracy-weighted average index, which simultaneously considers both clustering performance and clinical relevance. Using this framework, we performed a thorough evaluation and comparison of 11 state-of-the-art multi-omics clustering algorithms, including deep learning-based methods. By integrating the accuracy-weighted average index with computational efficiency, our analysis reveals that PIntMF demonstrates the best overall performance, making it a promising tool for molecular subtyping across a wide range of cancers.