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Background <p>The increasingly available multi-omics datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which exposure(s) exert the effect on outcome(s). However, existing methods fail to consider the case where (1) both exposures and mediators are potentially high-dimensional and (2) it is very likely that some important confounding variables are unmeasured or latent; both issues are quite common in practice. To the best of our knowledge, however, no methods have been developed to address these challenges with statistical guarantees.</p> Methods <p>In this article, we propose a new method for HIgh-dimensional LAtent-confounding Mediation Analysis (HILAMA) that considers both high-dimensional exposures and mediators, as well as the possible existence of latent confounding variables. HILAMA employs the <i>Decorrelating</i> &amp; <i>Debiasing</i> method to estimate the individual effects of exposures and mediators on the outcome. A column-wise regression strategy with parallel computing is considered to efficiently estimate the exposure-mediator effect matrix. HILAMA then applies the <i>MinScreen</i> procedure to eliminate non-significant pairs, and the Joint-Significance Testing (JST) method to compute <i>p</i>-values for the retained pairs, controlling the False Discovery Rate (FDR) using the Benjamini-Hochberg (BH) procedure.</p> Results <p>The proposed method is evaluated through extensive simulation experiments, demonstrating its improved stability in FDR control and superior power in finite sample size compared to existing competitive methods. Furthermore, our method is applied to the proteomics-radiomics data from ADNI, identifying some key proteins and brain regions related to Alzheimer’s disease. These empirical results demonstrate that HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions.</p> Conclusions <p>HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions.</p>

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HILAMA: High-dimensional multi-omics mediation analysis with latent confounding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Xinbo Wang,
  • Junyuan Liu,
  • Sheng’en Shawn Hu,
  • Zhonghua Liu,
  • Hui Lu,
  • Lin Liu

摘要

Background

The increasingly available multi-omics datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which exposure(s) exert the effect on outcome(s). However, existing methods fail to consider the case where (1) both exposures and mediators are potentially high-dimensional and (2) it is very likely that some important confounding variables are unmeasured or latent; both issues are quite common in practice. To the best of our knowledge, however, no methods have been developed to address these challenges with statistical guarantees.

Methods

In this article, we propose a new method for HIgh-dimensional LAtent-confounding Mediation Analysis (HILAMA) that considers both high-dimensional exposures and mediators, as well as the possible existence of latent confounding variables. HILAMA employs the Decorrelating & Debiasing method to estimate the individual effects of exposures and mediators on the outcome. A column-wise regression strategy with parallel computing is considered to efficiently estimate the exposure-mediator effect matrix. HILAMA then applies the MinScreen procedure to eliminate non-significant pairs, and the Joint-Significance Testing (JST) method to compute p-values for the retained pairs, controlling the False Discovery Rate (FDR) using the Benjamini-Hochberg (BH) procedure.

Results

The proposed method is evaluated through extensive simulation experiments, demonstrating its improved stability in FDR control and superior power in finite sample size compared to existing competitive methods. Furthermore, our method is applied to the proteomics-radiomics data from ADNI, identifying some key proteins and brain regions related to Alzheimer’s disease. These empirical results demonstrate that HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions.

Conclusions

HILAMA can effectively control FDR and provide valid statistical inference for high dimensional mediation analysis with latent confounding variables under certain assumptions.