metadeconfoundR: Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional
Data
Using non-parametric tests, naive associations between omics
features and metadata in cross-sectional data-sets are detected. In a second
step, confounding effects between metadata associated to the same omics
feature are detected and labeled using nested post-hoc model comparison
tests, as first described in
Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) <doi:10.1038/s41586-021-04177-9>.
The generated output can be graphically summarized using the built-in plotting function.
| Version: |
1.0.2 |
| Depends: |
R (≥ 3.5.0), detectseparation |
| Imports: |
lmtest, foreach, parallel, doParallel, stats, futile.logger, lme4, ggplot2, reshape2, methods, rlang |
| Suggests: |
pander, knitr, gridExtra, kableExtra |
| Published: |
2024-06-25 |
| DOI: |
10.32614/CRAN.package.metadeconfoundR |
| Author: |
Till Birkner 
[aut, cre],
Sofia Kirke Forslund-Startceva
[ctb] |
| Maintainer: |
Till Birkner <metadeconf at till-birkner.de> |
| BugReports: |
https://github.com/TillBirkner/metadeconfoundR/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/TillBirkner/metadeconfoundR |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
metadeconfoundR results |
Documentation:
Downloads:
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