数据集成的计算方法和Omics数据集中缺失值的归算
Yannis Schumann1, Antonia Gocke2,3, Julia E Neumann2,4
1IT-Department, Deutsches Elektronen-Synchroton DESY, Hamburg, Germany.
Proteomics
|December 31, 2024
概括
本综述提供了一个全面的指南,用于整合omics数据和赋值缺失值的计算方法. 它解决了批量效应和缺失数据的挑战,为研究人员提供了可靠的数据分析工具.
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
- 数据科学数据科学数据科学
背景情况:
- 奥米克数据 (DNA甲基组学,转录组学,蛋白质组学) 对于研究和临床决策至关重要.
- 批量效应和缺失值是阻碍OMIC数据集成和分析的重大挑战.
研究的目的:
- 为omics数据集成和缺失值赋值提供计算方法的全面概述.
- 定义缺失值机制,并为批量效应提出分类法,特别是在缺失数据的情况下.
主要方法:
- 系统的文献审查和自动化文档搜索.
- 32种数据集成方法和37种缺失值归算算法的描述.
- 对选择适当工具的定量方法的评估.
主要成果:
- 32种数据集成方法和37种归算算法的分类.
- 缺失价值机制的正式定义和批量效应的新型分类学.
- 讨论关于批量效应和omics数据中缺失值的相关性.
结论:
- 提出了从研究概念到最终分析的三步工作流程来进行OMIC数据分析.
- 为选择适当的归算和数据集成方法的建议.
- 确定未来的研究前景在奥米克斯数据预处理.
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