Package Recipe 'bioconductor-slalom'

recipe bioconductor-slalom

Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/slalom.html
License:: GPL-2
Recipe:: /bioconductor-slalom/meta.yaml

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8.

package bioconductor-slalom¶

Versions:

1.32.0-0, 1.28.0-0, 1.24.0-0, 1.22.0-0, 1.20.0-1, 1.20.0-0, 1.16.0-2, 1.16.0-1, 1.16.0-0,

1.32.0-0, 1.28.0-0, 1.24.0-0, 1.22.0-0, 1.20.0-1, 1.20.0-0, 1.16.0-2, 1.16.0-1, 1.16.0-0, 1.14.0-0, 1.12.0-1, 1.12.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.4.1-0, 1.4.0-0

Depends:

on bioconductor-gseabase >=1.72.0,<1.73.0
on bioconductor-gseabase >=1.72.0,<1.73.0a0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-bh
on r-ggplot2
on r-rcpp >=0.12.8
on r-rcpparmadillo
on r-rsvd

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-slalom

to add into an existing workspace instead, run:

pixi add bioconductor-slalom

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-slalom

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-slalom

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-slalom:<tag>

(see bioconductor-slalom/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-slalom/README.html)