DriverGenePathway

DriverGenePathway identifies cancer driver genes and pathways by integrating MutSigCV theory, information-entropy mutation categorization, multiple hypothesis tests, and de novo pathway detection for analysis of tumor mutational data.

Key Features:

• MutSigCV integration: Built on the theoretical framework of MutSigCV and uses information entropy to discover mutation categories.
• Hypothesis testing suite: Employs five statistical tests — beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test.
• Minimal core driver gene identification: Uses the hypothesis testing suite to identify minimal core driver genes.
• De novo pathway identification: Incorporates de novo methods to identify driver pathways and address mutational heterogeneity.
• R package implementation: Implemented as an R package for computational analysis.

Scientific Applications:

• TCGA-based cancer analysis: Analysis of diverse cancer types using The Cancer Genome Atlas (TCGA) mutational data.
• Driver validation: Validation and concordance assessment against the Cancer Gene Census to confirm known driver genes.
• Pathway discovery: Identification of novel and known driver pathways implicated in cancer development.

Methodology:

Integration of MutSigCV theoretical basis; discovery of mutation categories via information entropy; application of beta-binomial, Fisher combined p-value, likelihood ratio, convolution, and projection tests to identify minimal core driver genes; and use of de novo methods to detect driver pathways addressing mutational heterogeneity.