recipe bioconductor-multiclust

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/multiClust.html

License:

GPL (>= 2)

Recipe:

/bioconductor-multiclust/meta.yaml

Links:

biotools: multiclust, doi: 10.4137/cin.s38000

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

package bioconductor-multiclust

versions:

1.36.0-0,  1.32.0-0,  1.30.0-0,  1.28.0-0,  1.24.0-0,  1.22.0-0,  1.20.0-1,  1.20.0-0,  1.18.0-0, 

1.36.0-0,  1.32.0-0,  1.30.0-0,  1.28.0-0,  1.24.0-0,  1.22.0-0,  1.20.0-1,  1.20.0-0,  1.18.0-0,  1.16.0-0,  1.14.0-1,  1.14.0-0,  1.12.0-0,  1.10.0-0,  1.8.2-0

depends bioconductor-ctc:

>=1.80.0,<1.81.0

depends r-amap:

depends r-base:

>=4.4,<4.5.0a0

depends r-cluster:

depends r-dendextend:

depends r-mclust:

depends r-survival:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-multiclust

and update with::

   mamba update bioconductor-multiclust

To create a new environment, run:

mamba create --name myenvname bioconductor-multiclust

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

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

(see `bioconductor-multiclust/tags`_ for valid values for ``<tag>``)

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