recipe bioconductor-scfeaturefilter

A correlation-based method for quality filtering of single-cell RNAseq data

Homepage:

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

License:

MIT + file LICENSE

Recipe:

/bioconductor-scfeaturefilter/meta.yaml

An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise.

package bioconductor-scfeaturefilter

versions:

1.22.0-0,  1.20.0-0,  1.18.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.10.0-0,  1.8.0-0,  1.6.0-0, 

1.22.0-0,  1.20.0-0,  1.18.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.10.0-0,  1.8.0-0,  1.6.0-0,  1.4.0-1,  1.4.0-0,  1.2.1-0

depends r-base:

>=4.3,<4.4.0a0

depends r-dplyr:

>=0.7.3

depends r-ggplot2:

>=2.1.0

depends r-magrittr:

>=1.5

depends r-rlang:

>=0.1.2

depends r-tibble:

>=1.3.4

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-scfeaturefilter

and update with::

   mamba update bioconductor-scfeaturefilter

To create a new environment, run:

mamba create --name myenvname bioconductor-scfeaturefilter

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-scfeaturefilter:<tag>

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

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