CyCadas: accelerating interactive annotation and analysis of clustered cytometry data

Area of Science:

• Biotechnology
• Immunology
• Bioinformatics

Background:

• High-dimensional cytometry is crucial for biological and clinical research.
• Accurate annotation of cell populations in cytometry data is essential for reliable interpretation.
• Manual annotation of cytometry clusters is time-consuming and prone to errors.

Purpose of the Study:

• To develop a user-friendly, semi-automated tool for efficient and accurate annotation of cytometry data clusters.
• To streamline the analysis of high-resolution clustered cytometry data.
• To facilitate differential abundance detection across experimental conditions.

Main Methods:

• Introduction of Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas).
• CyCadas offers visual and computational guidance for cluster annotation.
• The tool supports data generated with Self-Organizing Map (SOM) clustering algorithms.

Main Results:

• CyCadas significantly reduces analysis time compared to manual methods.
• Features include a user-friendly interface, visual data exploration, and automated threshold estimation.
• Enables definition and annotation of cell phenotypes in a tree-based structure and calculates statistically interpreted cell population abundance.

Conclusions:

• CyCadas provides an efficient solution for annotating cytometry clusters and detecting differential abundance.
• The software is an ideal resource for researchers seeking to optimize cytometry workflows.
• CyCadas is available as open-source software.