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Updated: Feb 6, 2026

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Gene Expression Analyses in Human Follicles
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将规模不确定性纳入使用ALDEx2的微分表达式分析中
Scott J Dos Santos1, Gregory B Gloor1
1Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.
Current protocols
|February 4, 2026
概括
在测序数据中的差异丰度分析通过考虑样本规模的不确定性来改进. ALDEx2 在线阅读
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 不同的丰度和表达分析是测序数据的标准.
- 当前的方法往往缺乏真实样本规模的信息,导致技术变化误解.
- 现有的规范化技术对生物规模做出了有缺陷的假设,增加了错误发现率.
研究的目的:
- 为了证明将规模模型纳入RNA-seq,转录组和转录组数据的微分表达式分析.
- 突出规模建模对分析结果的影响.
- 介绍ALDEx2输出的可视化方法.
主要方法:
- 使用ALDEx2 R包构建和应用规模模型.
- 在批量转录基因组和转录基因组数据集上进行差异表达分析.
- 应用主要组件分析用于数据可视化.
主要成果:
- 规模模型减轻了正常化中的错误假设,降低了错误发现率.
- 整合规模模型可以提高微分表达式分析的准确性.
- 通过组合主要组件分析,可以有效地可视化ALDEx2的输出.
结论:
- 通过规模模型计算样本规模不确定性对于准确的差异丰度和表达分析至关重要.
- ALDEx2为将规模建模集成到标准生物信息学工作流程中提供了一个框架.
- 这种方法提高了来自高通量测序数据的发现的可靠性.
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