A general framework for powerful confounder adjustment in omics association studies.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad563
Asmita Roy, Jun Chen, Xianyang Zhang
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Abstract

Motivation: Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.

Results: This study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications.

Availability and implementation: R codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.

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组学关联研究中强大的混杂因素调整的通用框架。
动机:基因组数据受到各种混杂来源的影响,如人口统计学变量、生物学异质性和批量效应。为了在存在混杂因素的情况下识别与感兴趣变量相关的基因组特征,传统方法包括将混杂因素调整的回归模型拟合到每个基因组特征,然后进行多重性校正。结果:本研究表明,传统方法是次优的,并提出了一种新的二维错误发现率控制框架(2DFDR+),该框架比传统方法提供了显著的功率改进,适用于广泛的设置。2DFDR+使用边际独立性测试统计数据作为辅助信息来过滤出不太有希望的特征,并基于剩余特征中的条件独立性测试统计学来执行FDR控制。2DFDR+在给定感兴趣的协变和混杂因素的基因组变量的条件分布是任意和完全未知的情况下,从样本中提供(渐进)有效的推断。通过广泛的模拟和实际数据应用,展示了有希望的有限样本性能。可用性和实施:R代码和小插曲可在https://github.com/asmita112358/tdfdr.np.
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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