FunctanSNP:用于对密集SNP数据 (具有相互作用) 的功能分析的R包
Rui Ren1, Kuangnan Fang2, Qingzhao Zhang2,3
1Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States.
Bioinformatics (Oxford, England)
|December 7, 2023
概括
高维SNP数据分析具有挑战性,特别是在基因与环境相互作用方面. 新的R包FunctanSNP提供了一个功能分析策略,以简化这些复杂的遗传数据分析.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
背景情况:
- 高维单核酸多态 (SNP) 数据分析带来了重大的计算挑战,特别是在调查基因环境相互作用时.
- 现有的功能分析策略解决了维度问题,但缺乏可访问的软件实现.
研究的目的:
- 引入FunctanSNP,这是一个R包,旨在实现对密度测量SNP数据的功能分析.
- 为高级遗传数据分析提供用户友好和便携式软件解决方案.
主要方法:
- 开发了R包FunctanSNP,其中包括估计,识别和可视化工具.
- 使用模拟和现实世界的遗传数据集对包装进行严格的测试.
主要成果:
- FunctanSNP成功地实现了功能分析方法,有效地绕过了SNP数据的高维度.
- 广泛的验证证实了包装在分析遗传数据方面的可靠性和准确性.
结论:
- FunctanSNP解决了在功能遗传数据分析中需要实用的软件的需求.
- 该套件为研究人员分析SNP和其他密度测量的遗传数据提供了一个方便和可靠的工具.
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