TY - JOUR
T1 - CyCadas
T2 - accelerating interactive annotation and analysis of clustered cytometry data
AU - Hunewald, Oliver
AU - Demczuk, Agnieszka
AU - Longworth, Joseph
AU - Ollert, Markus
N1 - Funding
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.]
Publisher Copyright:
© The Author(s) 2024. Published by Oxford University Press.
PY - 2024/10/7
Y1 - 2024/10/7
N2 - 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.
AB - 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.
KW - Software
KW - Cluster Analysis
KW - Algorithms
KW - Flow Cytometry/methods
KW - Humans
KW - Single-Cell Analysis/methods
KW - Computational Biology/methods
UR - http://www.scopus.com/inward/record.url?scp=85206959314&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/39374546/
U2 - 10.1093/bioinformatics/btae595
DO - 10.1093/bioinformatics/btae595
M3 - Article
C2 - 39374546
AN - SCOPUS:85206959314
SN - 1367-4803
VL - 40
JO - Bioinformatics
JF - Bioinformatics
IS - 10
M1 - btae595
ER -