GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Burton, Ross J., Cuff, Simone M. ORCID: https://orcid.org/0000-0002-0546-3579, Morgan, Matt P., Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 and Eberl, Matthias ORCID: https://orcid.org/0000-0002-9390-5348 2023. GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data. Bioinformatics 39 (1) , btac751. 10.1093/bioinformatics/btac751

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Abstract

Motivation Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorise cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different ‘view’ of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. Results We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in scRNA-seq analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualisation and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions, and therapeutic and diagnostic options.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Mathematics
Publisher:	Oxford University Press
ISSN:	1367-4803
Funders:	MRC, Wellcome Trust
Date of First Compliant Deposit:	23 November 2022
Date of Acceptance:	21 November 2022
Last Modified:	06 Nov 2024 22:42
URI:	https://orca.cardiff.ac.uk/id/eprint/154423

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