RoDiCE

RoDiCE detects differential co-expression structures of protein complexes between tumor and normal tissues using copula-based statistical models to characterize cancer-specific proteome alterations.

Key Features:

• Robust Algorithm: Uses copula theory and copula-based statistical models to detect differential co-expression, providing robustness to noisy proteomic data and capturing non-linear dependencies beyond linear correlation.
• Cancer Complexome Analysis: Compares co-expression structures of protein complexes in cancerous versus normal tissues to reveal cancer-specific protein dysfunctions and aberrations within complexes.
• Application to Large-Scale Proteomic Data: Has been applied to large-scale proteomic datasets, including renal cancer studies, to identify significant protein complexes, regulatory signaling pathways, and potential drug targets.
• Improved Identification Accuracy: Enhances identification accuracy of differential co-expression structures and higher-order proteome interactions through copula-based differential testing.

Scientific Applications:

• Oncology research: Characterizing alterations in protein complex behavior to investigate disease mechanisms and nominate therapeutic targets.
• Proteomic biomarker and target discovery: Analysis of large-scale proteomic datasets (e.g., renal cancer) to identify key proteins, pathways, and potential drug targets.

Methodology:

Differential testing of protein co-expression patterns using copula-based statistical models as an alternative to conventional correlation techniques.