网络差分连接性分析。

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Annals of Applied Statistics Pub Date : 2022-12-01 Epub Date: 2022-09-26 DOI:10.1214/21-aoas1581
Sen Zhao, Ali Shojaie
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引用次数: 0

摘要

识别网络中的差异已经成为许多生物学应用中的一个典型问题。现有的方法试图通过直接比较两个网络的估计结构,或者测试两个群体中的协方差矩阵或逆协方差矩阵相同的零假设来实现这一目标。然而,正如我们在本文中所说明的,估计方法不能提供不确定性的测量,例如p值,而现有的测试方法可能会导致误导性的结果。为了解决这些缺点,我们提出了一个定性假设测试框架,该框架测试两个网络中的连接结构是否相同。如果目标是识别差异连接的节点或边,那么我们的框架尤其合适。现有的任何方法都无法检验这些假设并提供相应的不确定性度量。从理论上讲,我们证明了在适当的条件下,我们的建议在检验定性假设时正确地控制了I型错误率。根据经验,我们使用癌症基因组学中的模拟研究和应用来证明我们的提案的性能。
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NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS.

Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks, or testing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not provide measures of uncertainty, e.g., p-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a qualitative hypothesis testing framework, which tests whether the connectivity structures in the two networks are the same. our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.

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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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