调整基因特异性协变量以改善 RNA-seq 分析。

Adjusting for gene-specific covariates to improve RNA-seq analysis.

机构信息

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.

Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States.

出版信息

Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad498.

DOI:10.1093/bioinformatics/btad498

PMID:37589589

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10460482/

Abstract

SUMMARY

This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable.

AVAILABILITY AND IMPLEMENTATION

The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

摘要

本文提出了一种新的基于基因特异性协变量（如基因长度）的基因特异性假设检验的正错误发现率（pFDR）控制方法。我们假设零假设概率取决于协变量。在这种情况下，我们提出了一种拒绝规则，通过使用两种不同类型的零假设概率来考虑检验之间的异质性。我们通过 Storey 的 q 值框架为给定的拒绝规则估计了 pFDR。我们提供了一个关于第一类错误后验概率的条件，该条件等效地描述了我们的拒绝规则。我们还提出了一种通过交叉验证选择调谐参数的合适程序，该程序通过最大化宣布为显著的假设数量来最大化预期数量。模拟研究表明，在实际情况下，我们的方法与现有方法相当或更好。在数据分析中，我们支持我们的方法的前提，即零假设概率随基因特异性协变量而变化。