recipe bioconductor-globalancova

Global test for groups of variables via model comparisons



GPL (>= 2)




biotools: globalancova, doi: 10.1093/bioinformatics/btm531

The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.

package bioconductor-globalancova

(downloads) docker_bioconductor-globalancova



depends bioconductor-annotate:


depends bioconductor-annotate:


depends bioconductor-annotationdbi:


depends bioconductor-annotationdbi:


depends bioconductor-biobase:


depends bioconductor-biobase:


depends bioconductor-globaltest:


depends bioconductor-globaltest:


depends bioconductor-gseabase:


depends bioconductor-gseabase:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-corpcor:

depends r-dendextend:

depends r-vgam:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-globalancova

and update with::

   mamba update bioconductor-globalancova

To create a new environment, run:

mamba create --name myenvname bioconductor-globalancova

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-globalancova/tags`_ for valid values for ``<tag>``)

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