Automated descriptive cell type naming in flow and mass cytometry with CytoPheno

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Flow cytometry generates complex, high-dimensional data requiring advanced analysis.
• Automated clustering methods are common, but manual phenotyping of cell clusters is time-consuming and subjective.

Purpose of the Study:

• To develop an automated tool, CytoPheno, for assigning marker definitions and cell type names to cell clusters.
• To address the limitations of manual phenotyping in cytometry data analysis.

Main Methods:

• CytoPheno processes post-clustered expression data to assign positive/negative marker status.
• Marker names are standardized to Protein Ontology identifiers.
• Cell type names are assigned by matching marker descriptions to Cell Ontology terms.
• A graphical user interface (R Shiny) enhances accessibility.

Main Results:

• The algorithm successfully assigns marker definitions and cell type names to unidentified clusters.
• Performance was validated using benchmark data.
• The graphical user interface improves user interpretability.

Conclusions:

• CytoPheno provides a timely and unbiased solution for phenotyping cytometry data.
• The tool enhances the efficiency and accuracy of cell type identification.
• Automated phenotyping facilitates deeper insights from high-dimensional cytometry datasets.