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

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for analyzing cellular heterogeneity.
• Clustering algorithms are essential for identifying distinct cell populations in scRNA-seq data.
• Current clustering methods and ensemble approaches lack consistent performance and robust quality assessment across diverse datasets.

Purpose of the Study:

• To develop a novel ensemble clustering framework for scRNA-seq data that addresses limitations of existing methods.
• To improve the accuracy and reliability of cell population identification in scRNA-seq analysis.
• To provide a flexible framework optimizing for either mathematical cluster quality or biological interpretability.

Main Methods:

• Introduced ScEnsemble, a weighted hypergraph ensemble clustering framework.
• Integrated multiple base clustering algorithms using quality-based weighting, incorporating internal validation indices (Silhouette, Calinski-Harabasz, Davies-Bouldin, Dunn).
• Employed consensus algorithms (CSPA variants, MCLA, hypergraph spectral clustering) to partition the weighted hypergraph for final cluster generation.

Main Results:

• ScEnsemble matched or exceeded the performance of the best individual algorithm in 92% of metric-dataset combinations across five scRNA-seq datasets.
• Quality-based weighting significantly improved ensemble performance compared to unweighted ensembles in 84% of combinations.
• Biological validation on a breast cancer dataset confirmed that ScEnsemble clusters accurately represent known cell types.

Conclusions:

• ScEnsemble offers a principled approach to scRNA-seq clustering by leveraging algorithmic diversity.
• The framework enhances cell population identification, providing a robust solution for single-cell data analysis.
• ScEnsemble allows researchers to prioritize mathematical quality or biological relevance based on specific analytical goals.