揭开大脑表型的分子相关性:对零模型和测试统计数据的比较分析
Zhipeng Cao1, Guilai Zhan2, Jinmei Qin2
1Shanghai Xuhui Mental Health Center, Shanghai 200232, China; Department of Psychiatry, University of Vermont College of Medicine, Burlington VT, 05401, USA.
NeuroImage
|April 22, 2024
概括
这项研究评估了成像转录学的统计测试,发现竞争性零模型可以由于基因共同表达产生假阳性,而独立模型可能会错过双模式. 建议对竞争型号进行信号不敏感测试,对独立型号进行平均测试.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 遗传学 是一个
- 生物统计学 生物统计学
背景情况:
- 将基因表达与脑成像表型相关联,可以揭示认知和神经障碍的分子基础.
- 现有的成像转录学现有统计方法缺乏系统的性能评估.
- 用于评估转录关联的零模型 (竞争型和独立型) 和各种测试统计数据.
研究的目的:
- 系统地评估八个统计测试统计数据在成像转录学竞争性和独立的零模型中的性能.
- 识别这些分析中影响统计学意义的潜在偏差和混因素.
- 为选择适当的统计测试和零模型提供建议.
主要方法:
- 模拟的大脑图 (n=1,000) 和基因组 (n=500) 用于计算每个测试统计数据的显著性概率 (Psig).
- 八个测试统计数据 (平均值,平均绝对值,最大平均值,中位数,科尔莫戈罗夫-斯米尔诺夫 (KS),加权KS等) 进行了评估.
- 在竞争性零模型 (基因重新采样) 和独立的零模型 (大脑区域旋转) 中评估了性能.
主要成果:
- 竞争式的零模型可以产生错误的阳性,这是由于基因共同表达偏差.
- 独立的零模型可能会产生错误的阳性,因为它不考虑相关性分布特征,如双模式.
- 在竞争模型中,信号敏感测试受到共表达偏差的影响,而在独立模型中,中位数和信号不敏感测试对双模式偏差敏感.
- 在独立模型中的基于KS的统计数据是保守的,增加了虚假负值.
结论:
- 在具有竞争力的零模型中利用信号不敏感的测试统计数据 (例如,平均绝对值,最大平均值) 进行成像转录学.
- 在独立的零模型中使用平均测试统计数据.
- 考虑混匹配的零模型 (例如,协同表达匹配) 作为标准方法的替代方案.
- 这些发现指导了统计测试的选择,以提高成像转录学研究的可靠性.
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