ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• High-dimensional cytometry offers deep insights into immune system function in health and disease.
• Analyzing large, multiparametric cytometry datasets typically demands specialized computational expertise.
• Existing tools often present a barrier for researchers without extensive bioinformatics backgrounds.

Purpose of the Study:

• To introduce ImmunoCluster, an R package designed for accessible immune cell profiling.
• To facilitate the analysis of high-dimensional flow, liquid, and imaging mass cytometry data for non-specialists.
• To provide a scalable and versatile framework for immune heterogeneity analysis.

Main Methods:

• Development of ImmunoCluster, an open-source R package available on GitHub.
• Implementation of a three-stage computational protocol: data import/QC, dimensionality reduction/clustering, and annotation/differential testing.
• Scalability to millions of cells with integrated visualization and analytical tools.

Main Results:

• ImmunoCluster provides a user-friendly interface for complex immune data analysis.
• The package supports diverse high-dimensional cytometry data types (flow, liquid, imaging mass cytometry).
• It offers customizable plotting tools and analytical approaches tailored to user needs.

Conclusions:

• ImmunoCluster democratizes high-dimensional cytometry data analysis for a broader research community.
• The package enhances immune monitoring capabilities by simplifying cellular heterogeneity profiling.
• It supports the discovery of novel biomarkers and biological insights in various disease contexts.