MedZIsc: Statistical Framework for Co-Mediators of Zero-Inflated
Single-Cell Data
A causal mediation framework for single-cell data that incorporates two key features ('MedZIsc', pronounced Magics): (1) zero-inflation using beta regression and (2) overdispersed expression counts using negative binomial regression. This approach also includes a screening step based on penalized and marginal models to handle high-dimensionality. Full methodological details are available in our recent preprint by Ahn S and Li Z (2025) <doi:10.48550/arXiv.2505.22986>.
| Version: |
0.0.4 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MASS, betareg, glmnet |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2025-07-16 |
| Author: |
Seungjun Ahn 
[cre, aut],
Zhigang Li [ctb] |
| Maintainer: |
Seungjun Ahn <seungjun.ahn at mountsinai.org> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
MedZIsc results |
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