metadeconfoundR: Covariate-Sensitive Analysis of Cross-Sectional High-DimensionalData
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 |
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