CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Cluster Analysis; Humans; Single-Cell Analysis/methods; Computational Biology/methods; Software; Algorithms; Flow Cytometry/methods; Computational Biology; Flow Cytometry; Single-Cell Analysis; Statistics and Probability; Biochemistry; Molecular Biology; Computer Science Applications; Computational Theory and Mathematics; Computational Mathematics

Abstract :

[en] [en] MOTIVATION: Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. RESULTS: We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. AVAILABILITY AND IMPLEMENTATION: CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Disciplines :

Biochemistry, biophysics & molecular biology

Author, co-author :

Hunewald, Oliver ^✱; Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg ; Bioinformatics & AI, Department of Medical Informatics, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg

Demczuk, Agnieszka ^✱; University of Luxembourg ; Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg

LONGWORTH, Joseph ; University of Luxembourg ; Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg

OLLERT, Markus ; University of Luxembourg ; Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg ; Department of Dermatology and Allergy Centre, Odense University Hospital, 5000 Odense, Denmark

^✱ These authors have contributed equally to this work.

External co-authors :

Language :

English

Title :

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Publication date :

07 October 2024

Journal title :

Bioinformatics

ISSN :

1367-4803

eISSN :

1367-4811

Publisher :

Oxford University Press, England

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae595/59633397/btae595.pdf

Funders :

Luxembourg National Research Fund

Funding text :

This work was supported by the Luxembourg National Research Fund (FNR) through FNR-PRIDE program for doctoral education [14254520/i2TRON; A.D. and M.O.].

Available on ORBilu :

since 12 March 2026

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