Campello, R. J. G. B. | Search bioRxiv

bioRxiv2026

GraphHDBSCAN*: Graph-based Hierarchical Clustering on High Dimensional Single-cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is widely used to resolve cellular heterogeneity across thousands to millions of cells. A major challenge is to identify biologically meaningful cell populations while preserving their hierarchical organization, because broad cell types frequently split into more specialized subtypes. However, state-of-the-art approaches mostly focus on flat partitions and ignore the hierarchical structure of single-cell data. Here we introduce GraphHDBSCAN*, a graph-based, hyperparameter-free extension of HDBSCAN* that performs hierarchical density-based clustering on a graph representation of the data, enabling robust recovery of both single-level and hierarchical relationships in high-dimensional and sparse datasets. We evaluate GraphHDBSCAN* across multiple scRNA-seq datasets and show that it recovers biologically meaningful hierarchies that reveal fine-grained structure in complex data, including monocyte subpopulations. In addition, the method yields high-quality flat partitions that outperform widely used community-detection methods.

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bioRxiv2025

On the benchmarking of clustering algorithms and hyperparameter influence for cell type detection in single-cell RNA sequencing data.

Clustering single-cell RNA-seq (scRNA-seq) data and related protocols remains a major challenge due to high dimensionality, sparsity, and noise. Despite numerous benchmarking studies aiming to identify the most suitable clustering methods, many suffer from methodological flaws that can undermine their conclusions. A major challenge in benchmarking is selecting representative datasets that cover the diversity of scRNA-seq experiments and include laboratory-verified labels for reliable evaluation. Consistent preprocessing of all inputs to benchmarked algorithms is crucial, as it significantly impacts performance. Beyond selecting an algorithm, a thorough exploration of hyperparameters is also essential to assess robustness and identify configurations that maximize performance. We focus on proposing an improved benchmarking framework that addresses common methodological issues in prior studies. We illustrate our proposed methodology in a case study comparing the classic Leiden and Louvain clustering algorithms with extensive hyperparameters exploration on a carefully curated collection of real gold standard datasets. By evaluating clustering performance across different hyper-parameter selection scenarios, we show that benchmarking results can be misleading, either overestimating or underestimating performance depending on how the hyperparameter space is explored. In our illustrative case study, benchmarking results do not reveal any practically relevant performance differences between the Louvain and Leiden algorithms. In contrast, we show that overlooked factors such as graph construction and quality functions critically influence clustering outcomes, particularly un-der suboptimal settings of numerical hyperparameters--the neighbor-hood size k used for similarity graph construction and the resolution hyperparameter in graph-based clustering algorithms. While noticeable trends have been observed in terms of how different (dis)similarity functions affect performance, the impact of this choice is limited and, to some extent, overridden by the graph-building approach. Across different graphs, there is a noticeable trade-off between achieving optimal performance with ideally tuned numerical hyperparameters and maintaining robustness under more realistic, unsupervised, and suboptimal settings. All in all, the analysis of our illustrative benchmarking case study offers clear guidance and objective recommendations for practitioners in the field. Most importantly, as the main contribution of this manuscript, our proposed framework sets a foundation for more reliable scRNA-seq clustering evaluation and benchmarking in future studies.

bioinformatics↗