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特征特定的量子位规范化和特征特定的平均变异规范化提供了强大的双向分类和微阵列和RNAseq数据之间的特征选择性能.

Daniel Skubleny1, Sunita Ghosh2,3, Jennifer Spratlin2

  • 1Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2R3, Canada. skubleny@ualberta.ca.

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|March 29, 2024
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概括

特征特异量子规范化 (FSQN) 和特征特异平均变异规范化 (FSMVN) 有效地规范化跨平台基因表达数据. 这两种方法在机器学习分类方面都表现出相当的性能,最大限度地减少了技术偏差.

关键词:
跨平台规范化的跨平台规范化美国FSMVNNFSQN FSQN是什么意思功能选择 功能选择平均值是指平均值.微阵列的微阵列分子分类的分子分类.量子标准化规范化 量子标准化在RNAseqqq中使用.差异差异是指差异的差异.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 跨平台规范化对于整合微阵列和RNA-Seq全转录组数据至关重要.
  • 尽量减少技术偏差可以提高外部验证和机器学习模型培训集的可靠性.
  • 本研究将特征特异量级规范化 (FSQN) 与非特征特异平均方差规范化 (FSMVN) 方法进行比较.

研究的目的:

  • 评估和比较FSQN和FSMVN的性能,以实现跨平台基因表达数据的双向正常化.
  • 评估嵌套特征选择对规范化方法的影响.
  • 确定这些规范化技术是否可以消除不同技术平台之间的批量效应.

主要方法:

  • 特征特异量子规范化 (FSQN) 和特征特异平均变异规范化 (FSMVN) 应用于全转录组数据.
  • 在嵌套特征选择的背景下进行了双向规范化.
  • 主要组件分析 (PCA) 用于评估批量效应的去除.
  • 使用多变量线性回归分析来比较模型性能.

主要成果:

  • FSQN和FSMVN在临床上实现了对结肠CMS和乳腺PAM50分类的双向模型性能,具有或没有特征选择.
  • 据PCA证实,这两种方法都有效消除了与技术平台相关的批量效应.
  • 没有特征选择,FSQN和FSMVN与平台内部数据分布相比没有统计差异.
  • 在最佳特征选择下,FSQN和FSMVN的平衡精度在统计学上相当于平台内性能.

结论:

  • FSQN 和 FSMVN 在生成分子亚型的监督机器学习分类器方面同样有效.
  • 当在最佳建模条件下应用时,这些方法与平台内数据相比,提供相当的跨平台规范化准确性.
  • 建议在使用跨平台数据时谨慎使用,因为基于特定的分类问题和数据分布可能存在微妙的性能差异.