- recipe bioconductor-fccac
functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets
- Homepage:
- License:
Artistic-2.0
- Recipe:
- 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
- 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¶
Link to this page¶
Render an badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-fccac/README.html)