fCCAC
functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets
Bioconductor version: 3.24 · Package version: 1.39.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("fCCAC")Details
| Maintainer | Pedro Madrigal <pmadrigal@ebi.ac.uk> |
| Author | Pedro Madrigal [aut, cre] (ORCID: <https://orcid.org/0000-0003-1959-8199>) |
| License | Artistic-2.0 |
| URL | https://github.com/pmb59/fCCAC |
| Bug Reports | https://github.com/pmb59/fCCAC/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | ATACSeq, ChIPSeq, Coverage, Epigenetics, FunctionalGenomics, MNaseSeq, RNASeq, Sequencing, Software, Transcription |
| Package Short Url | https://bioconductor.org/packages/fCCAC/ |
Citation
From within R, enter citation("fCCAC"):
Pedro Madrigal. fCCAC: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets. doi:10.18129/B9.bioc.fCCAC, R package version 1.39.0, https://bioconductor.org/packages/fCCAC.
Generated from the package metadata; it may differ from the package's own citation.
Download
Follow the installation instructions to use this package in your R session.
| Source package | fCCAC_1.39.0.tar.gz |
| Windows binary (x86_64) | fCCAC_1.39.0.zip |
| macOS binary (arm64) | fCCAC_1.39.0.tgz |
| macOS binary (x86_64) | fCCAC_1.39.0.tgz |
Dependencies
Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid
Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown