对基于质谱的多omics数据集的规范化策略的评估
Chi Yen Tseng1, Jessica A Salguero2, Joshua D Breidenbach2
1Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA. chiyen_tseng@lanl.gov.
概括
概率系数规范化 (PQN) 和局部估计散射图平滑 (LOESS) 是多omics数据集成的顶级规范化方法. 这些方法有效地减少了错误,并保持了时间研究中的生物变异.
科学领域:
- 多omics数据集成多omics数据集成
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 数据规范化对于准确的多omics集成至关重要,减少系统错误和增强生物信号检测.
- 现有的研究往往忽略了时间过程数据,正常化可能会对时间趋势分析产生偏见.
- 这项研究解决了在使用综合实验设计的时间多学科研究中需要强大的规范化策略的需求.
研究的目的:
- 建立一个清晰的方法来评估对多omics数据集的规范化影响.
- 为了确定最可靠的标准化技术时间课程多omics数据.
- 将传统的规范化方法与机器学习方法 (SERRF) 相比较.
主要方法:
- 对人类心肌细胞和运动神经元的代谢学,脂质学和蛋白质学数据的分析.
- 细胞在一段时间内暴露于乙胆活性化合物.
- 基于质量控制 (QC) 的标准化方法的评估包括特征一致性和差异分析 (治疗和时间效应).
主要成果:
- 概率系数规范化 (PQN) 和LOESS QC证明了代谢学和脂质学的最佳性能.
- 对于蛋白质组学来说,PQN,中位数和LOESS正常化被证明是有效的.
- 这些选择的方法始终改善了质量控制指标,并保持了关键的生物差异 (时间或治疗相关).
结论:
- 推PQN和LoessQC用于在时间多态组合中进行代谢和脂质组合的正常化.
- PQN,中位数和Loess规范化适用于类似环境中的蛋白质组学.
- 这项研究为选择可靠的规范化方法提供了一个框架,用于时间课程多学科研究.
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