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Mediation analysis method review of high throughput data

  • Qiang Han , Yu Wang , Na Sun , Jiadong Chu , Wei Hu und Yueping Shen EMAIL logo
Veröffentlicht/Copyright: 29. November 2023
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Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 22 Heft 1

Abstract

High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.

Keywords: mediation analysis; high-dimensional; variable selection; dimension reduction; composite tests
Corresponding author: Yueping Shen, Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 RenAi Road, Suzhou 215123, Jiangsu Province, China, E-mail:

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 81973143

Acknowledgments

We would like to thank Editage editorial team for their English editing.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: National Natural Science Foundation of China (81973143).

  5. Data availability: Not applicable.

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Received: 2023-08-10
Accepted: 2023-11-11
Published Online: 2023-11-29

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2023-0031
Schlagwörter für diesen Artikel
mediation analysis; high-dimensional; variable selection; dimension reduction; composite tests

Artikel in diesem Heft

  1. Review
  2. Mediation analysis method review of high throughput data
  3. Research Articles
  4. When is the allele-sharing dissimilarity between two populations exceeded by the allele-sharing dissimilarity of a population with itself?
  5. Patterns of differential expression by association in omic data using a new measure based on ensemble learning
  6. Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization
  7. Randomized singular value decomposition for integrative subtype analysis of ‘omics data’ using non-negative matrix factorization
  8. A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction
  9. Accurate and fast small p-value estimation for permutation tests in high-throughput genomic data analysis with the cross-entropy method
  10. Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets
  11. A Bayesian model to identify multiple expression patterns with simultaneous FDR control for a multi-factor RNA-seq experiment
  12. A fast and efficient approach for gene-based association studies of ordinal phenotypes
  13. Software and Application Note
  14. CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data
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