Package Recipe 'bioconductor-sctensor'

recipe bioconductor-sctensor

Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/scTensor.html
License:: Artistic-2.0
Recipe:: /bioconductor-sctensor/meta.yaml

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

package bioconductor-sctensor¶

Versions:

2.20.0-0, 2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0,

2.20.0-0, 2.16.0-0, 2.12.0-0, 2.10.0-0, 2.8.0-0, 2.4.0-0, 2.2.0-0, 2.0.0-1, 2.0.0-0, 1.4.0-0, 1.2.0-0, 1.0.12-0

Depends:

on bioconductor-annotationdbi >=1.72.0,<1.73.0
on bioconductor-annotationhub >=4.0.0,<4.1.0
on bioconductor-biocstyle >=2.38.0,<2.39.0
on bioconductor-category >=2.76.0,<2.77.0
on bioconductor-dose >=4.4.0,<4.5.0
on bioconductor-gostats >=2.76.0,<2.77.0
on bioconductor-meshdbi >=1.46.0,<1.47.0
on bioconductor-meshr >=2.16.0,<2.17.0
on bioconductor-reactome.db >=1.95.0,<1.96.0
on bioconductor-reactomepa >=1.54.0,<1.55.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-schex >=1.24.0,<1.25.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-abind
on r-base >=4.5,<4.6.0a0
on r-biocmanager
on r-cctensor >=1.0.2
on r-checkmate
on r-crayon
on r-ggplot2
on r-heatmaply
on r-igraph
on r-knitr
on r-nntensor >=1.1.5
on r-outliers
on r-plotly
on r-plotrix
on r-rmarkdown
on r-rsqlite
on r-rtensor >=1.4.8
on r-tagcloud
on r-visnetwork

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-sctensor

to add into an existing workspace instead, run:

pixi add bioconductor-sctensor

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-sctensor

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-sctensor

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-sctensor:<tag>

(see bioconductor-sctensor/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-sctensor/README.html)