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

Wenda Zhang, Zichen Ma, Yen-Yi Ho (), Shuyi Yang, Joshua Habiger, Hsin-Hsiung Huang and Yufei Huang
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Wenda Zhang: Walmart Global Tech
Zichen Ma: Colgate University
Yen-Yi Ho: University of South Carolina
Shuyi Yang: University of South Carolina
Joshua Habiger: Oklahoma State University
Hsin-Hsiung Huang: University of Central Florida
Yufei Huang: University of Pittsburgh

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 3, No 1, 617 pages

Abstract: 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.

Keywords: Weighted p-value adjustment; Missing value; Incomplete Data; Integrative multi-omics analysis; Omnibus test (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00603-3

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