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

Motivation Clustering 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. 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. Availability and implementation GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster . Contact Ross.Burton@wales.nhs.uk

Clinical Cutaneous Features of Patients Infected With SARS-CoV-2 Hospitalized for Pneumonia: A Cross-sectional Study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a current pandemic worldwide. This virus can reach all organs and disturbs the immune system, leading to a cytokine storm in severe forms. We aimed to report cutaneous features among coronavirus disease 2019 (COVID-19) hospitalized patients. METHODS: We performed a cross-sectional study on 1 given day among all patients hospitalized in acute care for COVID-19 and included all patients with cutaneous features. Follow-up 48 hours later was obtained. RESULTS: Among 59 adult patients hospitalized on the day of the study in an infectious diseases ward for SARS-CoV-2 infection who were confirmed by molecular assay and/or radiological findings (computed tomography scan), 40 were included. Several cutaneous manifestations were found: macular exanthema (80%), face edema (32%), livedo (13%), urticarial rash (8%), purpura (5%), oral lichenoid lesions (33%), and conjunctivitis (18%). Cutaneous biopsy was performed in 17 patients. Histological findings showed mast cell hyperplasia (100%), superficial perivascular infiltrate of lymphocytes (94%), and superficial edema (47%) consistent with capillary leak. CONCLUSIONS: Various dermatological signs can be encountered during COVID-19. A macular rash was the most frequent. All cutaneous features could be related to a vascular leak process.