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普拉纳 (PRANA):一种R包,用于在存在额外的共变量的情况下进行差异性共表达网络分析
Seungjun Ahn1,2,3, Somnath Datta4
1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA. seungjun.ahn@mountsinai.org.
BMC genomics
|November 17, 2023
概括
我们开发了PRANA,这是一个R包,用于RNA测序数据中的差异网络分析. 它独特地结合了共变体,使得在复杂的生物条件下能够对差异连接的基因进行可靠的识别.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 随着RNA测序数据分析的进步,出现了新的统计方法,比如微分网络分析.
- 不同网络分析识别了基因相互作用,没有先前的途径知识.
- 现有的工具缺乏在微分网络分析中调整混共变量的能力.
研究的目的:
- 开发一种新的R包,用于RNA测序数据中的差异网络分析.
- 为了使多个共变量能够纳入微分网络分析.
- 提供用于识别差异连接基因的工具,同时调整混因素.
主要方法:
- 开发R包PRANA的开发.
- 实施一种新的伪值回归方法.
- 包括用于共变量调整和差异基因连接识别的功能.
主要成果:
- 普拉纳 (PRANA) 方便在微分网络分析中纳入多个共变量.
- 该套件提供了用于提取调整后的p值和系数估计的功能.
- 普拉纳可以在不同的生物条件之间识别不同连接的基因.
结论:
- 普拉纳解决了在微分网络分析中协变量调整的局限性.
- 该套件成功应用于慢性阻塞性肺病数据.
- 通过CRAN存储库,可以使用PRANA.
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