recipe bioconductor-fccac

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

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/fCCAC.html

License:

Artistic-2.0

Recipe:

/bioconductor-fccac/meta.yaml

Links:

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

Versions:

1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.16.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.8.0-0, 

1.24.0-0,  1.20.0-0,  1.18.0-0,  1.16.0-1,  1.16.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.8.0-0,  1.6.0-0,  1.2.0-0

Depends:

• bioconductor-complexheatmap >=2.14.0,<2.15.0

• bioconductor-genomation >=1.30.0,<1.31.0

• bioconductor-genomicranges >=1.50.0,<1.51.0

• bioconductor-iranges >=2.32.0,<2.33.0

• bioconductor-s4vectors >=0.36.0,<0.37.0

• r-base >=4.2,<4.3.0a0

• r-fda

• r-ggplot2

• r-rcolorbrewer

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-fccac

and update with:

conda update bioconductor-fccac

or use the docker container:

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

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

Download stats

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