recipe bioconductor-multiclust

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

Homepage

https://bioconductor.org/packages/3.11/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

1.18.0-0, 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 >=1.62.0,<1.63.0

• r-amap

• r-base >=4.0,<4.1.0a0

• r-cluster

• r-dendextend

• r-mclust

• r-survival

Required By

Installation

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

conda install bioconductor-multiclust

and update with:

conda update bioconductor-multiclust

or use the docker container:

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

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

Link to this page

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