recipe bioconductor-clustersignificance

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/ClusterSignificance.html

License:

GPL-3

Recipe:

/bioconductor-clustersignificance/meta.yaml

Links:

biotools: clustersignificance, doi: 10.1038/nmeth.3252

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.

package bioconductor-clustersignificance

(downloads) docker_bioconductor-clustersignificance

Versions:
1.38.0-0,  1.34.0-0,  1.30.0-0,  1.28.0-0,  1.26.0-0,  1.22.0-0,  1.20.0-0,  1.18.0-1,  1.18.0-0, 

1.38.0-0,  1.34.0-0,  1.30.0-0,  1.28.0-0,  1.26.0-0,  1.22.0-0,  1.20.0-0,  1.18.0-1,  1.18.0-0,  1.16.0-0,  1.14.0-0,  1.12.0-1,  1.12.0-0,  1.10.0-0,  1.8.2-0,  1.6.0-0

Depends:

  • on r-base >=4.5,<4.6.0a0

  • on r-pracma

  • on r-princurve >=2.0.5

  • on r-rcolorbrewer

  • on r-scatterplot3d

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-clustersignificance

to add into an existing workspace instead, run:

pixi add bioconductor-clustersignificance

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-clustersignificance

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-clustersignificance

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-clustersignificance:<tag>

(see bioconductor-clustersignificance/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.

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