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在大规模的多组学研究中纠正批量效应,使用基于参考材料的比率方法.

Ying Yu1, Naixin Zhang1, Yuanbang Mai1

  • 1State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.

Genome biology
|September 6, 2023
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概括
此摘要是机器生成的。

多组数据中的批量效应可能会误导结果. 基于比率的缩放方法有效地纠正了这些变化,为大规模研究中准确的数据集成和分析提供了广泛适用的解决方案.

关键词:
批量效应是一种批量效应.数据整合数据集成差异化表达的差异化表达计量学 计量学 计量学多种组合的多种组合.现象学 是一种现象学.预测 预测 预测四重奏家族四重奏的家庭.这是一个比率比率.参考材料是指参考材料的参考材料.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.
  • 数据科学数据科学数据科学

背景情况:

  • 多组学数据经常包含批量效应,即技术变化,如果不适当管理,可能会扭曲结果.
  • 现有的批量效应校正算法缺乏对其在不同omics类型,指标和应用中的性能进行全面评估.

研究的目的:

  • 综合评估和比较七个批量效应校正算法的性能,用于多组数据集成.
  • 确定最有效和广泛适用的方法来纠正批量效应,特别是当与生物信号混时.

主要方法:

  • 评估七个批量效应校正算法,使用与临床应用相关的指标,包括差异表达精度,预测模型稳定性和样本聚类.
  • 基于比率的缩放方法的实施和评估,使用并发配置的参考材料.

主要成果:

  • 与其他测试的算法相比,基于比率的方法显示出更高的有效性和更广泛的适用性.
  • 当批量效应与有趣的生物因素混时,这种方法尤其有利.
  • 开发了应用基于比率的方法在大型多经济学研究中的实用指南.

结论:

  • 多种数据易受批量效应的影响,但基于比率的缩放提供了一个有效的校正策略.
  • 本研究建立了一个基本的方法,以消除批量效应在一个比例尺度在多经济学分析.