GeneSurrounder

GeneSurrounder identifies disease-associated genes by integrating transcriptomic and gene expression data with pathway and regulatory network information to detect genes that drive dysregulation of neighboring genes.

Key Features:

• Integration of Expression and Network Data: Combines pathway network data and regulatory network information with gene expression and transcriptomic profiles to assess a gene's influence on the dysregulation of its network neighbors.
• Scoring System for Genes: Implements a scoring system that evaluates genes based on evidence that they influence dysregulation of adjacent genes within the network.
• Mechanistically Interpretable Results: Prioritizes genes that act as drivers of network dysregulation to produce mechanistically interpretable outputs relevant to disease mechanisms.
• Reproducibility Across Studies: Demonstrates improved reproducibility of identified genes across multiple studies of the same phenotype relative to competing methods.
• Facilitation of Experimental Follow-Up: Narrows candidate targets from large sets of related genes to aid selection of specific genes for experimental validation.
• Implementation: Implemented in R.

Scientific Applications:

• Systems Biology: Identifies driver genes within network contexts to inform systems-level interpretation of cellular dysregulation.
• Precision Medicine and Target Discovery: Supports identification of candidate diagnostic and therapeutic targets by pinpointing genes that drive network-level dysregulation.
• Complex Disease Research: Applicable to studies of complex diseases where integration of pathway and expression data is critical for understanding gene interactions.

Methodology:

Combines pathway and regulatory network data with gene expression/transcriptomic profiles, computes gene-level scores evaluating influence on adjacent genes, and is implemented in R.