scfdr：用经验贝叶斯控制稀疏相关矩阵的 FDR

False Discovery Rate Control for Sparse Correlation Matrix

AI 导读

研究提出一种基于经验贝叶斯框架的阈值策略，在构建稀疏相关矩阵时控制非零相关中的 FDR，并估计非零相关里的预期假阳性数。该方法兼容任意相关度量，作者称其在保留真实生物学相关上比基于 Frobenius 范数最小化的交叉验证阈值更灵敏；相关 R 包 scfdr 已在 github.com/ariefgusnanto/scfdr 开放。

论文摘要

Motivation

In genomic data analysis, high-dimensional correlation matrices pose challenges in interpreting complex relationships among genomic features, particularly due to overfitting. Traditional approaches generate sparse correlation matrices by setting correlations below a predetermined threshold to zero, with the threshold typically determined through Frobenius norm minimisation via cross-validation.

Results

In this study, we propose a novel alternative thresholding strategy based on an empirical Bayes framework that controls the false discovery rate (FDR) among non-zero correlations. This method offers two key advantages: first, it is compatible with any correlation measure and, second, it provides an estimation of the expected false positives among non zero correlations. By formulating the construction of sparse correlation matrix as both an estimation and an inference problem, our approach enhances interpretability and demonstrates greater sensitivity in retaining true biological correlations than threshold selection based on the minimisation of Frobenius norm.

Availability and Implementation

The relevant R package called scfdr is available from github.com/ariefgusnanto/scfdr