Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples.

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Mass cytometry generates high-dimensional single-cell expression data from clinical samples.
• Traditional analysis methods are insufficient for these complex datasets.
• Novel computational approaches are needed to interpret single-cell data.

Purpose of the Study:

• To describe detailed methods for analyzing high-dimensional single-cell immune cell expression data.
• To demonstrate the application of network analysis techniques for biological insights.
• To provide a framework for analyzing immune cell tumor infiltrates.

Main Methods:

• Utilized mass cytometry for high-dimensional single-cell data generation.
• Implemented dimensionality reduction techniques.
• Applied network analysis methods, specifically SCAFFoLD and CITRUS.

Main Results:

• Detailed step-by-step protocols for SCAFFoLD and CITRUS analyses are provided.
• Demonstrated the analysis of immune cell expression in clinical samples.
• Showcased the utility of network analysis for understanding immune cell infiltration in tumors.

Conclusions:

• Network analysis offers powerful approaches for interpreting complex single-cell data.
• SCAFFoLD and CITRUS are effective tools for dissecting immune cell populations.
• These methods advance the analysis of immune cell tumor infiltrates for clinical research.