一本关于基于关联的维度减小方法的入门书,用于多omics分析.
Tim Downing1,2, Nicos Angelopoulos1
1Pirbright Institute, Pirbright, Surrey, UK.
Journal of the Royal Society, Interface
|October 11, 2023
概括
本综述探讨了多omics数据集成,从单个样本中结合不同的分子概况. 它指导研究人员选择合适的计算和统计方法来分析复杂的数据集.
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:
- 欧米技术的进步使生物样本的综合分子分析成为可能.
- 从同一样本中整合多个omic数据源 (多个omics) 提供了更深入的生物学见解.
研究的目的:
- 审查基于关联的维度缩小和网络方法,用于多omics数据集成.
- 为选择合适的分析方法和多学科研究的实验设计提供指导.
主要方法:
- 专注于基于关联的维度缩小,用于单个,配对和多个omics数据集.
- 简要介绍了整合三种或更多种omics数据集的网络方法.
- 将方法与相关的R包进行链接,以便在实践中实现.
主要成果:
- 提供了多学科整合技术的结构化概述.
- 为研究人员提供实用的路线图和实验设计考虑.
- 突出了具有较大的样本大小的人口多组的潜力.
结论:
- 研究人员可以更有效地导航新兴的多学科整合方法.
- 对多样化的欧米克数据集进行适当的分析可以增强生物学理解.
- 促进了在大型研究中应用多主题方法.
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