Covariate-Sensitive Analysis of Cross-Sectional High-Dimensional Data
Overview
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.
- "confounded confounders" will now be removed from the status label of a confounded variable.
- Example: If the association of D with feature Y is confounded by C1 and C2, but the association of C1 with Y is in turn confounded by C2, then only C2 will be listed as confounder of Y~D
- improvments in some logging messages
- featureMat is now checked for non-numeric columns at start-up.
From NEWS in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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- PRs merged (all time)
- 95%
- Median days to close, 50 most recently updated closed issues
- 189
- Median days to close, 50 most recently updated closed PRs
- 0
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Dependencies
Installing metadeconfoundR adds 83 packages (47 with compiled code, 69 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, igraph 4.8 MB.
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Package metadata
- First published
- 2024-06-25
- Total releases
- 2 / 2 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 63 KB / 2 files
- Download size
- 1.4 MB
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