Package Recipe 'bioconductor-rscudo'

recipe bioconductor-rscudo

Signature-based Clustering for Diagnostic Purposes

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/rScudo.html
License:: GPL-3
Recipe:: /bioconductor-rscudo/meta.yaml

SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set.

package bioconductor-rscudo¶

Versions:

1.26.0-0, 1.22.0-0, 1.18.0-0, 1.16.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0,

1.26.0-0, 1.22.0-0, 1.18.0-0, 1.16.0-0, 1.14.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-1

Depends:

on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-base >=4.5,<4.6.0a0
on r-igraph
on r-stringr

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-rscudo

to add into an existing workspace instead, run:

pixi add bioconductor-rscudo

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-rscudo

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-rscudo

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-rscudo:<tag>

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