FuseSOM

FuseSOM performs unsupervised clustering of highly multiplexed in situ imaging cytometry assays using a correlation-based multiview self-organizing map architecture that integrates correlation-based metrics.

Key Features:

• Self-Organizing Map Architecture: FuseSOM employs a Self Organizing Map architecture for clustering analysis.
• Multiview Integration: FuseSOM integrates correlation-based metrics through a Multiview approach.
• Correlation-Based Metrics: FuseSOM utilizes correlation-based metrics for characterizing cell types.

Scientific Applications:

• Cell Type Characterization: FuseSOM characterizes cell types in highly multiplexed in situ imaging cytometry assays.
• IMC Dataset Clustering: FuseSOM clusters highly multiplexed in situ imaging cytometry datasets including IMC (Imaging Mass Cytometry) data.

Methodology:

FuseSOM combines a Self Organizing Map architecture with Multiview integration of correlation-based metrics.