recipe bioconductor-fccac

functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets







biotools: fccac

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.

package bioconductor-fccac

(downloads) docker_bioconductor-fccac



depends bioconductor-complexheatmap:


depends bioconductor-genomation:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends r-base:


depends r-fda:

depends r-ggplot2:

depends r-rcolorbrewer:



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-fccac

and update with::

   mamba update bioconductor-fccac

To create a new environment, run:

mamba create --name myenvname bioconductor-fccac

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

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