Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators

Clicks: 1
ID: 319331
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #793 of 825 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 825 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Mediation analysis plays a crucial role in understanding how exposure variables influence health outcomes via intermediate variables, or mediators, in environmental studies. When analyzing a large number of environmental exposures, such as chemical mixtures or pollutants, together with multiple potential mediators such as metabolites, advanced methodologies are necessary to accurately separate direct and indirect effects. This paper proposes a novel mediation analysis method based on Sparse Canonical Correlation Analysis (SCCA), designed specifically for settings where both exposures and mediators are high-dimensional. The effectiveness of the proposed method is evaluated through simulation studies and an application to real-world data. RESULTS: The proposed SCCA-based mediation framework improved identification of relevant mediators and pathways in simulation studies, particularly in high-dimensional and noisy settings. The two-step screening extension further enhanced feature selection while maintaining stable estimation. In the real-data application, the method identified interpretable exposure-metabolite pathways associated with MELD score, with several pathways showing moderate selection stability and robustness to potential unmeasured confounding. AVAILABILITY: The R code for implementing the proposed method and the simulation studies is available at https://github.com/MaggieLi2001/HDM-SCCA2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7166871067 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xincheng Li, Maiying Kong, Matthew Ryan Smith, Yongliang Liang, Sami Teeny, ViLinh Thi Ly, Young-Mi Go, Niharika Samala, Dean P Jones, Jianzhu Luo, Walter H. Watson, Craig J. McClain, Vatsalya Vatsalya, Gyongyi Szabo, Srinivasan Dasarathy, Mack Mitchell, Laura Nagy, Bruce Barton, Matthew C. Cave, Hongmei Jiang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag474
URL
https://doi.org/10.1093/bioinformatics/btag474
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.