InterCellar

InterCellar predicts and analyzes cell–cell communication from single-cell transcriptomic (scRNA-seq) data to characterize ligand–receptor interactions and their associated biological pathways.

Key Features:

• Predictive Analysis of Cell–Cell Communication: Uses computational algorithms to predict cellular interactions based on ligand–receptor expression dynamics inferred from scRNA-seq data.
• Customized Biological Interpretation: Performs tailored analytical steps to enhance the biological interpretation of predicted interactions.
• Multiple Visualization Options: Generates diverse visualizations to explore predicted interactions and their pathway associations.
• Integration with Biological Pathways: Maps ligand–receptor interactions to biological pathways to contextualize cellular communication.
• Data-Driven Comparative Analysis: Enables comparison of cell–cell communication patterns across different conditions for data-driven insights.

Scientific Applications:

• COVID-19 analysis: Applied to study cellular communication patterns in COVID-19, identifying patterns of interaction and molecular signals.
• Melanoma research: Applied to analyze cell–cell communication in melanoma to reveal interaction patterns and associated pathways.
• Hypothesis generation: Supports generation of hypotheses about disease mechanisms and potential therapeutic targets by linking interactions to biological functions and pathways.

Methodology:

Predicts cell–cell interactions by analyzing ligand–receptor pairs identified in single-cell transcriptomic (scRNA-seq) data and links these interactions to biological pathway information.