recipe r-leapp

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

Homepage:

https://CRAN.R-project.org/package=leapp

License:

GPL3 / GPL-2.0-or-later

Recipe:

/r-leapp/meta.yaml

package r-leapp

(downloads) docker_r-leapp

versions:

1.3-2,  1.3-1,  1.3-0,  1.2-4,  1.2-3,  1.2-2,  1.2-1,  1.2-0

depends bioconductor-sva:

depends r-base:

>=4.3,<4.4.0a0

depends r-corpcor:

depends r-mass:

requirements:

Installation

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 r-leapp

and update with::

   mamba update r-leapp

To create a new environment, run:

mamba create --name myenvname r-leapp

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 quay.io/biocontainers/r-leapp:<tag>

(see `r-leapp/tags`_ for valid values for ``<tag>``)

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