调整还是不调整. 如何利用基因组分析中的灵活性导致过度乐观
Milena Wünsch1,2, Christina Sauer1,2, Moritz Herrmann1,2
1Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, LMU Munich, Munich, Germany.
Biometrical journal. Biometrische Zeitschrift
|December 19, 2024
概括
研究人员可能无意中偏向基因组分析 (GSA) 结果,通过选择产生首选结果的分析参数. 这种"桃挑选"导致乐观偏见和基因表达数据中不可复制的发现.
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:
- 基因组分析 (GSA) 对于解释高通量基因表达数据至关重要.
- 许多GSA方法和参数选择提供了灵活性,但可能会导致用户的不确定性.
- 缺乏基于证据的指导可能会鼓励低于最佳的分析实践.
研究的目的:
- 调查由于用户驱动的参数选择而导致GSA过度乐观结果的可能性.
- 提高对GSA"桃挑选"的认识,类似于经典假设测试中的问题.
- 为减轻GSA和相关领域过度乐观主义提供实际建议.
主要方法:
- 模拟了一个研究人员选择分析变体以实现首选结果.
- 评估了具有不同输入和内部参数的流行的GSA方法.
- 利用两个基因表达基因基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因表达基因分析
主要成果:
- 该研究发现,在GSA.中存在过度乐观的巨大潜力.
- 某些常用的GSA方法,尽管受到批评,但显示出高度倾向于偏见的结果.
- 在GSA中的参数灵活性可能会导致无法复制的发现.
结论:
- 过度乐观是基因组分析中相当令人担忧的一个问题.
- 提供了推,以促进强大的和可复制的GSA.
- 这些发现扩展到GSA以外的其他复杂数据分析场景.
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