GmicR

GmicR uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell as a hypothesis generating tool.

Key Features:

• WGCNA Integration: Detects relationships with Gene Modules identified by Weighted Gene Co-expression Network Analysis (WGCNA).
• xCell Integration: Incorporates immune cell signatures defined by xCell.
• Bayesian Network Learning: Applies bayesian network learning to infer relationships between gene modules and immune cell signatures.
• Network Generation: Generates a Gene-Module Immune-Cell network (GMIC).

Scientific Applications:

• Hypothesis Generation: Serves as a hypothesis generating tool for exploring gene module-immune cell relationships.
• Immune Cell Signature Analysis: Investigates connections between transcriptional modules and immune cell populations.

Methodology:

Bayesian network learning is applied to detect relationships between Gene Modules from WGCNA and immune cell signatures from xCell.