CytoPheno: Automated descriptive cell type naming in flow and mass cytometry.

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Cytometry enables measurement of numerous cellular markers, leading to complex datasets.
• Manual phenotyping of cell clusters is time-consuming, subjective, and prone to errors.

Purpose of the Study:

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

Main Methods:

• Developed an algorithm for automated phenotyping of cytometry data.
• Standardized marker names to Protein Ontology identifiers.
• Matched marker descriptions to cell types using the Cell Ontology.
• Integrated the tool into an R Shiny graphical user interface.

Main Results:

• CytoPheno successfully assigns marker definitions and cell type names to cell clusters.
• The tool demonstrated performance on benchmark data.
• The graphical user interface enhances user accessibility and interpretability.

Conclusions:

• CytoPheno provides a timely and unbiased solution for phenotyping post-clustered cytometry data.
• The automated approach reduces subjectivity and errors associated with manual methods.
• CytoPheno can significantly aid researchers in interpreting complex flow cytometry datasets.