fCCAC

DOI: 10.18129/B9.bioc.fCCAC  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see fCCAC.

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

Bioconductor version: 3.17

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.

Author: Pedro Madrigal [aut, cre]

Maintainer: Pedro Madrigal <pmadrigal at ebi.ac.uk>

Installation

To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("fCCAC")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("fCCAC")

 

PDF	R Script	fCCAC Vignette
PDF		Reference Manual
Text		NEWS

Details

biocViews	ATACSeq, ChIPSeq, Coverage, Epigenetics, FunctionalGenomics, MNaseSeq, RNASeq, Sequencing, Software, Transcription
Version	1.26.0
In Bioconductor since	BioC 3.4 (R-3.3) (7 years)
License	Artistic-2.0
Depends	R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid
Imports	fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils
LinkingTo
Suggests	RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	fCCAC_1.26.0.tar.gz
Windows Binary	fCCAC_1.26.0.zip
macOS Binary (x86_64)	fCCAC_1.26.0.tgz
macOS Binary (arm64)	fCCAC_1.26.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/fCCAC
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/fCCAC
Bioc Package Browser	https://code.bioconductor.org/browse/fCCAC/
Package Short Url	https://bioconductor.org/packages/fCCAC/
Package Downloads Report	Download Stats