Clustering trees: a visualization for evaluating clusterings at multiple resolutions
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Abstract
Clustering techniques are widely used in the analysis of large datasets to group together samples with similar properties. For example, clustering is often used in the field of single-cell RNA-sequencing in order to identify different cell types present in a tissue sample. There are many algorithms for performing clustering, and the results can vary substantially. In particular, the number of groups present in a dataset is often unknown, and the number of clusters identified by an algorithm can change based on the parameters used. To explore and examine the impact of varying clustering resolution, we present clustering trees. This visualization shows the relationships between clusters at multiple resolutions, allowing researchers to see how samples move as the number of clusters increases. In addition, meta-information can be overlaid on the tree to inform the choice of resolution and guide in identification of clusters. We illustrate the features of clustering trees using a series of simulations as well as two real examples, the classical iris dataset and a complex single-cell RNA-sequencing dataset. Clustering trees can be produced using the clustree R package, available from CRAN and developed on GitHub.
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Optimal cluster resolution was determined with guidance of the clustree v0.5.0 package.
“Optimal cluster resolution was determined with guidance of the clustree v0.5.0 package73.”
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Augur was applied at various clustering resolutions to evaluate the robustness of cell type prioritizations to the resolution at which neuronal subpopulations were defined.
“To evaluate the robustness of cell type prioritizations to the resolution at which neuronal subpopulations were defined in the snRNA-seq data, we applied Augur at various clustering resolutions, and visualized the resulting cell type prioritizations both on a hierarchical clustering tree81 of neuron subpopulations and as a progression of UMAPs.”
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Cluster stability for the integrated whole olfactory bulb dataset was analyzed with Clustree on a range of resolution values (0.4 to 1.4), with 0.6 yielding the most stable set of clusters.
“Cluster stability was analyzed with Clustree on a range of resolution values (0.4 to 1.4), with 0.6 yielding the most stable set of clusters (107).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [107]
Our sample sizes for experiments were chosen based on our prior publication using APP/PS1 mice, which defined the expectations for the effect size and variance.
“Our sample sizes for experiments were chosen based on our prior publication using APP/PS1 mice 58 , which defined the expectations for the effect size and variance 55 .”
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- Authors
- Zappia, L., & Oshlack, A.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Oshlack, Alicia
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-07-01
- Volume
- 7
- Issue
- 7
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC6057528
- OpenAlex
- W2952100718
- Cited by
- 1,064
- References
- 29
- Citations by year
- 2026: 215 · 2025: 235 · 2024: 246 · 2023: 130 · 2022: 118 · 2021: 61 · 2020: 42 · 2019: 12 · 2018: 5
- FWCI
- 17.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- GigaScience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- National Health and Medical Research Council (APP1126157)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- clustering tree, cluster validation, hierarchical clustering, cluster visualization, single-cell RNA sequencing, cell type annotation, number of clusters, Iris dataset
- MeSH terms
- Algorithms, Computer Simulation, Data Interpretation, Statistical, Humans, Immune System, Leukocytes, Mononuclear, Mitochondria, Software, Models, Statistical, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Principal Component Analysis
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