纵向omics数据分析:方法和应用
Ali Reza Taheriyoun1, Allen Ross1, Abolfazl Safikhani2
1Department of Biostatistics and Bioinformatics, The George Washington University, Washington, DC 20052, USA.
分析纵向奥米克数据 (LOD) 需要先进的统计方法来理解生物动态. 本综述指导研究人员通过复杂的LOD分析的各种方法,涵盖建模,分类和新兴技术.
科学领域:
- 生物统计学 生物统计学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 纵向奥米克数据 (LOD) 对于研究生物过程和随时间的疾病进展至关重要.
- 分析LOD带来了不平衡,高维度和非高斯分布等挑战.
研究的目的:
- 审查统计和计算方法用于纵向的奥米克数据分析.
- 突出LOD各种方法的应用和局限性.
主要方法:
- 对线性混合模型 (LMM) 和通用线性混合模型 (GLMM) 以及它们的扩展进行讨论.
- 探索功能数据分析 (FDA),分类,生存建模和多变量方法.
- 涵盖新兴主题,如数据集成,集群和基于网络的建模.
主要成果:
- 关于奥米克数据分析的最先进方法的分类.
- 强调不同方法如何解决纵向数据的特定特征.
- 在LOD建模和假设测试中识别挑战和解决方案.
结论:
- 有效分析复杂的LOD需要强大的和量身定制的统计策略.
- 本综述为研究人员提供了一条指导方针,帮助他们选择合适的LOD分析方法.
- 了解LOD动态是推动生物见解和临床应用的关键.
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