recipe bioconductor-catalyst

Cytometry dATa anALYSis Tools

Homepage:

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

License:

GPL (>=2)

Recipe:

/bioconductor-catalyst/meta.yaml

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

package bioconductor-catalyst

versions:

1.26.0-0,  1.24.0-0,  1.22.0-0,  1.18.0-0,  1.16.0-0,  1.14.0-1,  1.14.0-0,  1.12.1-0,  1.10.0-0, 

1.26.0-0,  1.24.0-0,  1.22.0-0,  1.18.0-0,  1.16.0-0,  1.14.0-1,  1.14.0-0,  1.12.1-0,  1.10.0-0,  1.8.6-0,  1.6.0-0

depends bioconductor-complexheatmap:

>=2.18.0,<2.19.0

depends bioconductor-consensusclusterplus:

>=1.66.0,<1.67.0

depends bioconductor-flowcore:

>=2.14.0,<2.15.0

depends bioconductor-flowsom:

>=2.10.0,<2.11.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-scater:

>=1.30.0,<1.31.0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-circlize:

depends r-cowplot:

depends r-data.table:

depends r-dplyr:

depends r-drc:

depends r-ggplot2:

depends r-ggrepel:

depends r-ggridges:

depends r-gridextra:

depends r-matrix:

depends r-matrixstats:

depends r-nnls:

depends r-purrr:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-rtsne:

depends r-scales:

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-catalyst

and update with::

   mamba update bioconductor-catalyst

To create a new environment, run:

mamba create --name myenvname bioconductor-catalyst

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-catalyst:<tag>

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

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