Exact model-free function inference using uniform marginal counts for null population

Yiyi Li

doi:10.1093/bioinformatics/btaf121

2025-03-20

Yiyi Li

Mingzhou Song *

DOI：10.1093/bioinformatics/btaf121

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动机识别因果关系是一种基础性的科学探究。然而，当前的因果推断方法通常关注方向性而非统计显著性。一个结果是，偶然模式的不均衡边缘分布可能达到完美的方向性得分。结果为了克服这些问题，我们设计了具有连续性校正的均匀精确函数检验（UEFTC），用于检测两个离散随机变量之间的函数依赖关系。其零假设是两个变量在统计上独立。与使用观测边缘分布作为零假设总体的相关检验不同，我们通过嵌入的均匀方阵来定义零假设总体。我们还提出了一种快速算法来完成该检验。在具有真实值的实验数据集中，UEFTC表现出准确的方向性、低偏差以及优于替代方法的稳健统计行为。我们发现，在工程酵母菌株中，基因TCB2对β-雌二醇剂量的反应是非单调的。在患有环境性肠道功能障碍的人类十二指肠中，我们发现了与病理相关的反协同甲基化的CpG位点，这些位点位于基因POU2AF1和LSP1附近；此类活动代表同一基因上的协同甲基化和去甲基化，此前尚未有报道。UEFTC在精确无模型函数推断方面显著提升了数据驱动知识发现的有效性。可用性与实现一个开源R包UniExactFunTest实现了所提出的均匀精确函数检验，可通过CRAN获取，其DOI为10.32614/CRAN.package.UniExactFunTest。用于重现图表的计算机代码可在补充文件UEFTC-main.zip中找到。

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DNA METHYLATION

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Bioinformatics

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5.4

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1.3K

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17.9W

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