SCENERY

SCENERY reconstructs protein interaction networks from single-cell flow and mass cytometry data to enable network-level analysis of cellular signaling and protein interactions.

Key Features:

• Analysis framework: A structured suite covering data preprocessing, statistical analysis, and network reconstruction workflows for cytometry datasets.
• Machine learning integration: Incorporates standard and advanced machine learning algorithms optimized for flow and mass cytometry to infer protein interaction networks.
• Visualization and reporting: Produces network visualizations and exportable figures and reports suitable for publication and further analysis.
• Modular R architecture: Implemented in R with a modular design that supports submission and integration of custom network reconstruction methods.

Scientific Applications:

• Single-cell protein interaction mapping: Reconstruction of protein interaction networks at the single-cell level from flow and mass cytometry data.
• Cellular biology studies: Analysis of intracellular signaling and protein network organization within heterogeneous cell populations.
• Immunology and systems biology: Investigation of immune cell signaling and systems-level protein interaction patterns across conditions or treatments.

Methodology:

Computational steps explicitly include data preprocessing, statistical analysis, application of machine learning algorithms tailored for single-cell cytometry, and network reconstruction algorithms implemented within an R-based modular framework.