对微生物组数据的交叉相关性的构成意识估计
Ib Thorsgaard Jensen1,2, Luc Janss3, Simona Radutoiu1
1Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
PloS one
|June 28, 2024
概括
新的方法,SparCEV和SparXCC,量化微生物丰度 (操作分类单位,OTU) 和外部变量或其他组成数据之间的相关性. 这些新的方法提高了微生物组和转录组分析的准确性,特别是在代过程中.
科学领域:
- 微生物组研究的研究.
- 生物信息学是一种生物信息学.
- 统计建模 统计建模
背景情况:
- 微生物组研究通常分析微生物丰度 (操作分类单位,OTU) 之间的相关性.
- 现有的方法很难将OTU丰度与外部变量或其他组成数据集相关联.
- 测序数据的组成性质需要专门的分析方法.
研究的目的:
- 引入新的方法,SparCEV和SparXCC,用于量化OTU丰度和外部变量之间的相关性.
- 启用OTU丰度与连续的表型数据或组合数据集 (如转录学) 之间的相关性分析.
- 评估新方法的性能与现有的转换和交叉相关技术相比.
主要方法:
- 开发了SparCEV (与外部变量的稀疏相关性) 和SparXCC (组合数据之间的稀疏交叉相关性).
- 实施了SparCEV和SparXCC的代版本,以解决潜在的偏差.
- 通过使用经验性皮尔森交叉相关性,将SparCEV/SparXCC与天真 (日志,日志-TSS) 和高级 (CLR,VST) 转换进行了比较.
主要成果:
- 除了密集相关性矩阵外,CLR和VST转换的性能优于天真方法.
- 在少量OTU中,SparCEV和SparXCC表现优于CLR/VST.
- 代程序提高了SparCEV/SparXCC的准确性,除了接近零的平均相关性或密集矩阵的情况下.
结论:
- SparCEV和SparXCC提供了可靠的方法来将微生物丰度与外部数据相关联.
- 代方法提升了准确性,使这些方法对微生物组和多组学研究有价值.
- 这些方法促进了微生物群落内外复杂关系的分析.
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