recipe bioconductor-catalyst

Cytometry dATa anALYSis Tools

Homepage:

https://bioconductor.org/packages/3.17/bioc/html/CATALYST.html

License:

GPL (>=2)

Recipe:

/bioconductor-catalyst/meta.yaml

Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation.

package bioconductor-catalyst

versions:

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, 

1.24.0-0,  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:

>=2.16.0,<2.17.0

depends bioconductor-consensusclusterplus:

>=1.64.0,<1.65.0

depends bioconductor-flowcore:

>=2.12.0,<2.13.0

depends bioconductor-flowsom:

>=2.8.0,<2.9.0

depends bioconductor-s4vectors:

>=0.38.0,<0.39.0

depends bioconductor-scater:

>=1.28.0,<1.29.0

depends bioconductor-singlecellexperiment:

>=1.22.0,<1.23.0

depends bioconductor-summarizedexperiment:

>=1.30.0,<1.31.0

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-magrittr:

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:

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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