Package Recipe 'bioconductor-feast'

recipe bioconductor-feast

FEAture SelcTion (FEAST) for Single-cell clustering

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

Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.

package bioconductor-feast¶

Versions:

1.18.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0, 1.2.0-2, 1.2.0-1, 1.2.0-0,

1.18.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0, 1.2.0-2, 1.2.0-1, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-sc3 >=1.38.0,<1.39.0
on bioconductor-sc3 >=1.38.0,<1.39.0a0
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 bioconductor-tscan >=1.48.0,<1.49.0
on bioconductor-tscan >=1.48.0,<1.49.0a0
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 libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-irlba
on r-matrixstats
on r-mclust

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-feast

to add into an existing workspace instead, run:

pixi add bioconductor-feast

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-feast

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-feast

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-feast:<tag>

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