- recipe bioconductor-multiclust
multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles
- Homepage:
https://bioconductor.org/packages/3.20/bioc/html/multiClust.html
- License:
GPL (>= 2)
- Recipe:
- 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:
1.40.0-0,1.36.0-0,1.32.0-0,1.30.0-0,1.28.0-0,1.24.0-0,1.22.0-0,1.20.0-1,1.20.0-0,1.40.0-0,1.36.0-0,1.32.0-0,1.30.0-0,1.28.0-0,1.24.0-0,1.22.0-0,1.20.0-1,1.20.0-0,1.18.0-0,1.16.0-0,1.14.0-1,1.14.0-0,1.12.0-0,1.10.0-0,1.8.2-0- Depends:
on bioconductor-ctc
>=1.84.0,<1.85.0on r-amap
on r-base
>=4.5,<4.6.0a0on r-cluster
on r-dendextend
on r-mclust
on r-survival
- 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-multiclust
to add into an existing workspace instead, run:
pixi add bioconductor-multiclust
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-multiclust
Alternatively, to install into a new environment, run:
conda create -n envname bioconductor-multiclust
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-multiclust:<tag>
(see bioconductor-multiclust/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:
[](http://bioconda.github.io/recipes/bioconductor-multiclust/README.html)
