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

Abstract

MotivationClustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, is typically used to categorise cells into subpopulations of similar phenotype. 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, which to our knowledge has not been applied to cytometry. ResultsWe 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. Availability and implementationGeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster. ContactRoss.Burton@wales.nhs.uk