spongEffects

spongEffects infers ceRNA subnetworks and computes sample-specific module scores from gene expression to prioritize miRNA-related regulatory modules for cancer patient stratification and biomarker discovery.

Key Features:

• Subnetwork Inference: Infers subnetworks (modules) within competing endogenous RNA (ceRNA) networks to delineate regulatory interactions.
• Sample-Specific Scores: Computes regulatory activity scores specific to each sample for inferred subnetworks.
• Integration with Machine Learning: Produces module scores suitable for downstream machine learning tasks such as tumor classification.
• Biomarker Prioritization: Prioritizes ceRNA modules as potential biomarkers using module scores derived from gene expression data alone, enabling application when miRNA expression data is unavailable.

Scientific Applications:

• Tumor Classification: Supports tumor classification and has been applied to classify breast cancer subtypes.
• Subtype-Specific Regulatory Interactions: Identifies subtype-specific interactions within ceRNA networks to characterize regulatory heterogeneity.
• Patient Stratification: Enables stratification of patients based on module regulatory activity profiles.
• Biomarker Discovery: Facilitates discovery and prioritization of ceRNA-based biomarkers impacting cancer subtype biology.

Methodology:

Analyzes gene expression data to infer subnetworks from ceRNA networks and computes sample-specific regulatory activity scores for those subnetworks, with scores usable in classification and biomarker identification and derivable without miRNA expression data.