cytomapper

cytomapper visualizes highly multiplexed imaging data to enable spatial profiling of biomarkers and cellular phenotypes from technologies such as imaging mass cytometry.

Key Features:

• Dual-Layer Visualization: Displays pixel-intensity images alongside segmented object information to represent marker expression and cellular morphology.
• Integration with Bioconductor: Implements data structures and functions that leverage Bioconductor infrastructure for image analysis and single-cell data processing.
• Parallel Image Processing: Supports visualization of datasets ranging from single images to hundreds of images for comparative analysis across large cohorts.
• Composite Imaging and Feature Highlighting: Generates composite images of multiple markers and colors segmentation masks based on specified cellular features.
• Cell Type Outlining: Outlines selected cells within images based on cell type or phenotypic criteria to aid morphological assessment.
• Hierarchical Gating: Supports hierarchical gating of cells according to marker expression levels for selection and annotation of cell populations.

Scientific Applications:

• Imaging Mass Cytometry Cohort Analysis: Applied to imaging mass cytometry datasets from cohorts, including studies of type 1 diabetes patients and healthy individuals, to visualize biomarker distributions and cellular phenotypes.
• Cell Phenotyping and Spatial Profiling: Facilitates detailed cell phenotyping and spatial profiling analyses by combining marker expression imaging with segmentation-based cellular information.

Methodology:

Implemented in the R programming language as a Bioconductor package.