recipe bioconductor-cytokernel

Differential expression using kernel-based score test

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/cytoKernel.html

License:

GPL-3

Recipe:

/bioconductor-cytokernel/meta.yaml

cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns.

package bioconductor-cytokernel

versions:

1.12.0-0,  1.8.0-0,  1.6.0-0,  1.4.0-1,  1.4.0-0,  1.0.0-2,  1.0.0-1,  1.0.0-0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0a0

depends bioconductor-complexheatmap:

>=2.22.0,<2.23.0

depends bioconductor-complexheatmap:

>=2.22.0,<2.23.0a0

depends bioconductor-s4vectors:

>=0.44.0,<0.45.0

depends bioconductor-s4vectors:

>=0.44.0,<0.45.0a0

depends bioconductor-summarizedexperiment:

>=1.36.0,<1.37.0

depends bioconductor-summarizedexperiment:

>=1.36.0,<1.37.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc:

>=13

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx:

>=13

depends r-ashr:

depends r-base:

>=4.4,<4.5.0a0

depends r-circlize:

depends r-data.table:

depends r-dplyr:

depends r-magrittr:

depends r-rcpp:

depends r-rlang:

requirements:

additional platforms:

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 bioconductor-cytokernel

and update with::

   mamba update bioconductor-cytokernel

To create a new environment, run:

mamba create --name myenvname bioconductor-cytokernel

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/bioconductor-cytokernel:<tag>

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

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