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/3.11/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

1.16.0-01.14.0-01.12.0-11.12.0-01.10.0-01.8.2-01.6.0-0

Depends
Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-clustersignificance

and update with:

conda update bioconductor-clustersignificance

or use the docker container:

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

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