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.