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DriverDetector: An R package providing multiple statistical methods for cancer driver genes detection and tools for
Zeyuan Wang1, Hong Gu1, Pan Qin1
1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Lingshui Street, Dalian, 116024, Liaoning, China.
Heliyon
|January 16, 2025
Summary
Identifying cancer driver genes is challenging. DriverDetector, an R package, uses a voting strategy integrating 11 methods for robust detection, improving consistency and aiding targeted drug development.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Cancer driver gene identification is complex due to tumor heterogeneity and gene interactions.
- Existing algorithms lack consistent results, necessitating improved detection methods.
- Increasing availability of sequencing data drives the need for advanced computational approaches.
Purpose of the Study:
- To develop DriverDetector, an R package for reliable cancer driver gene detection and analysis.
- To integrate multiple statistical methods for robust identification of driver genes.
- To provide a user-friendly workflow for cancer genomics research.
Main Methods:
- Developed a background mutation rate module using covariate space distance and binomial tests.
- Integrated 11 driver gene identification methods, including novel Fisher's method variants.
- Applied a voting strategy combining 10 statistical methods for enhanced prediction consistency.
Main Results:
- Verification on 12 TCGA datasets showed significant variation in gene sets identified by individual methods.
- The voting strategy demonstrated superior consistency in driver gene prediction.
- Sample size was found to significantly impact the number of predicted driver genes.
Conclusions:
- DriverDetector offers a robust and user-friendly approach to cancer driver gene detection.
- The voting strategy enhances prediction reliability and consistency.
- This tool can facilitate early cancer diagnosis and targeted therapy development.
