recipe bioconductor-cola

A Framework for Consensus Partitioning






Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner.

package bioconductor-cola

(downloads) docker_bioconductor-cola



depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-complexheatmap:


depends bioconductor-complexheatmap:


depends bioconductor-impute:


depends bioconductor-impute:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-brew:

depends r-circlize:


depends r-clue:

depends r-cluster:

depends r-crayon:

depends r-digest:

depends r-doparallel:

depends r-dorng:

depends r-eulerr:

depends r-foreach:

depends r-getoptlong:

depends r-globaloptions:


depends r-httr:

depends r-irlba:

depends r-knitr:


depends r-markdown:


depends r-matrixstats:

depends r-mclust:

depends r-microbenchmark:

depends r-png:

depends r-rcolorbrewer:

depends r-rcpp:


depends r-skmeans:

depends r-xml2:



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-cola

and update with::

   mamba update bioconductor-cola

To create a new environment, run:

mamba create --name myenvname bioconductor-cola

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<tag>

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

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