组合转换可以合理地将表型相关值引入稀疏特征中
George I Austin1,2, Tal Korem2,3
1Department of Biomedical Informatics, Columbia University Irving Medical , New York, NY, USA.
bioRxiv : the preprint server for biology
|January 20, 2025
概括
瘤微生物组数据中的稀有特征可能会因数据转换而与表型相关,例如中心日志比率 (CLR),不一定表明机器学习管道问题. 这一发现挑战了微生物组分析中信息泄露的说法.
科学领域:
- 微生物组研究 微生物组研究
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 最近的论点表明,如果在批次校正后稀疏特征成为表型相关的,与瘤相关的微生物组数据分析是无效的.
- 这引发了人们对处理或机器学习管道中的潜在信息泄露的担忧.
研究的目的:
- 调查稀有特征是否与表型相关,必然表明微生物组数据处理或机器学习管道存在问题.
- 证明样本智能的转换可以在没有信息泄露的情况下创建这样的关联.
主要方法:
- 利用中心日志比率 (CLR) 转换,这是对组成微生物组数据的常用方法.
- 分析了合成和阴道微生物组数据集.
- 重新分析了Gihawi等人先前认为有问题的特征.
主要成果:
- 证明了CLR转换,一个样本智能的操作,可以导致最初稀疏的特征与表型相关联.
- 这种关联发生在CLR中使用的几何平均值与表型相关时,特别是对于零值的常见归算策略.
- 证明了在CLR中观察到的这种现象,可以作为必要信息泄露的主张的反例.
结论:
- 在像CLR这样的样本智能转换后,表型相关的稀疏特征的出现并不能独立地证明机器学习管道中的信息泄漏.
- 这种观察可以来自转换的性质和数据特征,不一定来自管道工件.
- 强调需要谨慎解释多变量微生物组数据中的个体特征.
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