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MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.
- Source :
-
Genes . Jun2022, Vol. 13 Issue 6, p1049-1049. 17p. - Publication Year :
- 2022
-
Abstract
- Background: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). Methods: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. Results: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. Conclusions: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HUMAN microbiota
*PERFORMANCE standards
Subjects
Details
- Language :
- English
- ISSN :
- 20734425
- Volume :
- 13
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Genes
- Publication Type :
- Academic Journal
- Accession number :
- 157748898
- Full Text :
- https://doi.org/10.3390/genes13061049