CGV

CGV integrates and visualizes multi-omic cancer genomics and pharmacogenomic datasets to enable analyses such as gene set analysis, clustering, survival analysis, and interrogation of pre-calculated drug response scores for cancer research.

Key Features:

• Integration and visualization: Integrates diverse cancer genomics and pharmacogenomic datasets and visualizes combined multi-omic data.
• Customizable sample selection: Enables sample selection based on predefined clinic-pathological features across multiple datasets.
• Gene set analysis: Performs gene set analysis to identify significant biological pathways or processes.
• Clustering: Performs clustering to group samples by genetic similarity and identify distinct cancer subtypes.
• Survival analysis: Performs survival analysis to correlate genomic features with patient outcomes.
• Pre-calculated drug response scores: Provides access to pre-calculated drug response scores derived from pharmacogenomic data for evaluation of therapeutic sensitivity.

Scientific Applications:

• Cancer genomics profiling: Dissect mechanisms of cancer development and progression through integrated genomic and pharmacogenomic profiling.
• Drug discovery and target identification: Support identification of potential therapeutic targets by combining genomic features with pharmacogenomic drug response data.
• Personalized medicine: Enable development of personalized treatment strategies by linking genomic features to drug response and survival outcomes.

Methodology:

Integration of multi-omic and pharmacogenomic datasets; customizable sample selection using predefined clinic-pathological features; gene set analysis, clustering, and survival analysis; use of pre-calculated drug response scores derived from pharmacogenomic data.