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集群-部分最小平方 (c-PLS) 回归分析:适用于miRNA和代谢学数据
Julia Kuligowski1, Álvaro Pérez-Rubio2, Marta Moreno-Torres3
1Neonatal Research Group, Health Research Institute La Fe, Valencia, Spain.
Analytica chimica acta
|December 4, 2023
概括
这项研究引入了集群部分最小平方 (c-PLS),这是一个新的方法,通过根据生物相关性对变量进行分组来分析omics数据. 在复杂的数据集中,C-PLS提高了对驱动模型预测的生物因素的理解.
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
- 系统生物学 系统生物学
背景情况:
- 高通量技术产生大量的奥米克数据集 (基因组学,转录组学,代谢组学),需要复杂的分析.
- 路径分析识别了生物影响,但通常需要多变量技术,如局部最小平方 (PLS).
- 像间隔-PLS (iPLS) 这样的现有变量选择方法对于特征缺乏连续维度的omics数据是不理想的.
研究的目的:
- 为了引入一种新的变量选择方法,集群部分最小平方 (c-PLS),用于分析omics数据.
- 评估生物相关变量组对多变量模型预测性能的联合影响.
- 提高对推动复杂生物系统模型预测的生物因素的理解.
主要方法:
- 为变量选择开发并应用集群部分最小方程 (c-PLS).
- 利用了来自肝脏组织活检的miRNomic和代谢学数据集.
- 分析了24个样本,在临床研究中分析了乳脂病.
主要成果:
- C-PLS有效地评估了生物定义的变量集群 (例如,miRNA调节的途径,脂类) 的影响.
- 该方法有助于识别与独立变量相关的生物过程.
- 实现了对影响模型性能的生物因素的优先考虑,增强了预测理解.
结论:
- 通过利用生物背景,C-PLS提供了一种强大的方法来分析omics数据.
- 它通过关注生物学上相关的变量组来提高多变量模型的可解释性.
- 该策略与PLS模型进行了测试,显示了扩展到其他多变量技术的潜力.
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