微生物组数据的差异网络连接分析,经过对临床共变量进行调整,使用刀伪值.
Seungjun Ahn1,2,3, Somnath Datta4
1Department of Biostatistics, University of Florida, Gainesville, FL, USA.
BMC bioinformatics
|March 19, 2024
概括
我们开发了SOHPIE-DNA,这是一种用于微生物群差异网络分析的新型回归方法,可以考虑临床因素. 这种方法改善了回忆和F1分数,识别了与炎症和疲劳相关的微生物种类.
科学领域:
- 微生物组研究的研究.
- 生物信息学是一种生物信息学.
- 网络分析 网络分析
背景情况:
- 下一代测序使微生物组数据的差异网络 (DN) 分析成为可能.
- DN分析比较了各种条件下的微生物共同丰富网络.
- 现有的DN方法忽略了临床共变量,如年龄和BMI.
研究的目的:
- 为微生物组DNA分析引入一种新的基于回归的方法.
- 将额外的临床共变量纳入DN分析.
- 提高微生物组网络分析的准确性和回忆力.
主要方法:
- 通过伪值信息和差异网络分析估计 (SOHPIE-DNA) 开发了统计方法.
- 采用了一种回归技术,使用杰克刀伪值.
- 将SOHPIE-DNA应用于模拟和真实微生物组数据集.
主要成果:
- 与现有方法相比,SOHPIE-DNA显示出更好的回忆和F1得分.
- 该方法保持了可比的精度和准确性.
- 确定了与肠道炎症和癌症患者疲劳相关的微生物种群.
结论:
- SOHPIE-DNA是微生物组DNA分析的第一个回归框架.
- 可通过共变量信息预测网络连接.
- 一个R包 (SOHPIE) 和源代码是公开的.
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