recipe bioconductor-consica

consensus Independent Component Analysis






consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.

package bioconductor-consica

(downloads) docker_bioconductor-consica



depends bioconductor-biocparallel:


depends bioconductor-go.db:


depends bioconductor-graph:




depends bioconductor-summarizedexperiment:


depends bioconductor-topgo:


depends r-base:


depends r-fastica:


depends r-ggplot2:

depends r-pheatmap:

depends r-rfast:

depends r-sm:

depends r-survival:



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-consica

and update with::

   mamba update bioconductor-consica

To create a new environment, run:

mamba create --name myenvname bioconductor-consica

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-consica/tags`_ for valid values for ``<tag>``)

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