COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms

Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies.Author summary: We developed COPS (Clustering algorithms for Omics-driven Patient Stratification), a computational platform to tackle challenges in disease subtype discovery using single or multi-omics data. COPS employs innovative methods for assessing clustering stability and utilizes the Pareto optimal criterion to find solutions balancing clustering evaluation metrics and clinical relevance. Our comprehensive comparison across several multi-omic cancer datasets demonstrated the efficacy of both pathway-based and non-pathway-based methods in different contexts. Furthermore, we introduced two new methods utilizing pathway graph kernels and multiple kernel learning for pathway-based patient stratification. In our benchmarking study, we observed that different clustering algorithms yielded solutions with different trade-offs between clustering stability, association with known subtypes, and significance of survival differences between the clusters. This work underscores the need for future clustering algorithms to simultaneously address both stability and clinical relevance.