Multi-omics Integrative Analysis for Incomplete Data Using Weighted p-Value Adjustment Approaches

TL;DR

This paper introduces a new method for analyzing multi-omics data with missing values by adjusting p-values, improving statistical power compared to existing methods.

Contribution

A novel integrative framework using p-value weight adjustment to handle incomplete multi-omics data is proposed.

Findings

The proposed framework achieves higher statistical power than complete case analysis or multiple imputation.

The method was successfully applied to a study of preterm infant birth weights using DNA methylation and mRNA data.

Simulation analyses confirmed the effectiveness of the p-value weight adjustment approach.

Abstract

The advancements in high-throughput technologies provide exciting opportunities to obtain multi-omics data from the same individuals in a biomedical study, and joint analyses of data from multiple sources offer many benefits. However, the occurrence of missing values is an inevitable issue in multi-omics data because measurements such as mRNA gene expression levels often require invasive tissue sampling from patients. Common approaches for addressing missing measurements include analyses based on observations with complete data or multiple imputation methods. In this paper, we propose a novel integrative multi-omics analytical framework based on p-value weight adjustment in order to incorporate observations with incomplete data into the analysis. By splitting the data into a complete set with full information and an incomplete set with missing measurements, we introduce mechanisms to derive weights and weight-adjusted p-values from the two sets. Through simulation analyses, we demonstrate that the proposed framework achieves considerable statistical power gains compared to a complete case analysis or multiple imputation approaches. We illustrate the implementation of our proposed framework in a study of preterm infant birth weights by a joint analysis of DNA methylation, mRNA, and the phenotypic outcome. Supplementary materials accompanying this paper appear online.

The online version contains supplementary material available at 10.1007/s13253-024-00603-3.

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