NetBoxR

NetBoxR implements the NetBox algorithm in R to identify biological process modules by integrating genetic mutations and copy number alterations with human interaction networks for cancer and cell biology research.

Key Features:

• Network-Based Approach: Combines prior biological knowledge with network clustering to detect functional modules within human interaction networks.
• Integration of Multiple Data Types: Integrates molecular data types including genetic mutations and copy number alterations to prioritize candidate processes.
• Pre-loaded Human Interaction Network (HIN): Includes a pre-compiled HIN assembled from HPRD, Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map.
• Obviation of Functionally Curated Gene Sets: Identifies functional modules directly from interaction data without requiring predefined gene sets.
• Implementation in R: Provides an R package implementation of the NetBox algorithm and associated network-clustering routines.

Scientific Applications:

• Cancer genomics: Prioritizes candidate cancer-related processes from large-scale sequencing datasets such as TCGA and ICGC by mapping mutations and copy number alterations onto networks.
• Cell biology: Discovers functional modules relevant to cellular processes by analyzing interaction networks and perturbation data.

Methodology:

Applies the NetBox algorithm by integrating prior biological knowledge with network-clustering algorithms on human interaction networks and mapping genetic mutations and copy number alterations to identify and characterize functional modules.