产生对应的数据用于omics模拟
Jianing Yang1,2, Gregory R Grant1,3, Thomas G Brooks1
1Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS computational biology
|September 5, 2025
概括
模拟与相关的omics数据对于准确的计算管道基准测试至关重要. 我们的高斯配方方法有效地生成现实,依赖的奥米克数据,改善方法评估.
科学领域:
- 计算生物学
- 生物信息学
- 统计模型
背景情况:
- 对计算管道进行基准测试至关重要.
- 欧米克数据经常显示测量特征之间的相关性,由于计算挑战,这些特征在模拟中经常被忽视.
- 现有的模拟方法难以有效地结合特征依赖性.
研究的目的:
- 引入高效的方法来生成与相关的测量数据.
- 为了证明将相关性纳入基准研究的影响.
- 提供一个灵活的R包来模拟依赖的奥米克数据.
主要方法:
- 基于高斯配方模型开发了三种模拟方法.
- 使用共变矩阵分解 (对角和低等级) 以提高计算效率.
- 对于各种边际分布,在R包中实施的"依赖式"方法.
主要成果:
- 当包括特征依赖时,DESeq2结果的变异性增加.
- 在推断基因对基因依赖的昼夜时间方面证明了CYCLOPS的性能改善.
- 验证了对数据模拟的相关性的重要性,以便进行准确的比较.
结论:
- 相关的欧米克数据的有效模拟是可以实现的,对于稳健的基准测试至关重要.
- 整合特征依赖性可以显著影响omics分析工具的性能.
- 独立数据包提供了一个实际的解决方案,用于生成现实的,依赖的数据.
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