ScEnsemble: weighted hypergraph ensemble clustering for single-cell RNA sequencing.

backgroundSingle-cell RNA sequencing enables detailed profiling of cellular heterogeneity, with clustering serving as a critical step for identifying distinct cell populations. However, no single clustering algorithm consistently outperforms others across diverse datasets, creating uncertainty in robust cell population identification. Existing ensemble methods either treat all algorithms equally or employ simple filtering strategies, failing to account for varying solution quality across different datasets.

resultsWe present ScEnsemble, a weighted hypergraph ensemble clustering framework that integrates multiple base algorithms through quality-based weighting. ScEnsemble constructs a hypergraph where edges represent cluster co-assignments, weighted by internal validation indices including Silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index. Multiple consensus algorithms partition the weighted hypergraph to produce final clusters, including CSPA variants with hierarchical clustering and community detection methods, MCLA with multiple consensus strategies, and hypergraph spectral clustering. Benchmarking across five scRNA-seq datasets demonstrates that the best ensemble configuration matched or exceeded the best individual algorithm in 23 of 25 metric-dataset combinations (92%). Quality-based weighting further improved upon unweighted ensembles in 21 of 25 combinations (84%). Biological validation on a breast cancer tumor microenvironment dataset demonstrated that ScEnsemble clusters correspond to known cell types as confirmed by independent gene-set scoring.

conclusionsScEnsemble provides a principled solution for scRNA-seq clustering that leverages algorithmic diversity rather than forcing selection of a single method. The framework enables researchers to optimize for either mathematical cluster quality or biological interpretability based on their analytical priorities, addressing a fundamental challenge in single-cell data analysis.