ICN

ICN detects interconnected communities in gene co-expression networks (GCNs) by identifying correlated subsets of genes across communities to reveal complex regulatory interactions.

Key Features:

• Interconnected Community Detection: Identifies pairs of communities as interconnected when subsets of genes from one community are correlated with subsets from another, providing a nuanced representation of the gene co-expression matrix.
• Advanced Algorithmic Approach: Employs efficient algorithms based on an advanced graph norm shrinkage technique to improve accuracy and computational performance on large networks.
• Empirical Validation and Application: Validated through extensive simulation studies and applied to RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study to identify biological pathways involved in immune evasion.

Scientific Applications:

• Gene Regulatory Mechanism Analysis: Uncovers interconnected communities to provide deeper insights into complex gene regulatory networks governing expression.
• Pathway Identification in Cancer: Identifies key pathways and interactions relevant to disease mechanisms, exemplified by detection of immune evasion–related pathways in AML using TCGA RNA-seq data.

Methodology:

Extracts interconnected communities from GCNs by evaluating correlations between gene subsets across communities and applying efficient algorithms based on an advanced graph norm shrinkage technique.