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fCCAC

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

Bioconductor version: 3.23 · Package version: 1.38.0

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

MaintainerPedro Madrigal <pmadrigal@ebi.ac.uk>
AuthorPedro Madrigal [aut, cre] (ORCID: <https://orcid.org/0000-0003-1959-8199>)
LicenseArtistic-2.0
URLhttps://github.com/pmb59/fCCAC
Bug Reportshttps://github.com/pmb59/fCCAC/issues
Downloads rank566
Source branchRELEASE_3_23
biocViewsATACSeq, ChIPSeq, Coverage, Epigenetics, FunctionalGenomics, MNaseSeq, RNASeq, Sequencing, Software, Transcription

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagefCCAC_1.38.0.tar.gz
Windows binary (x86_64)fCCAC_1.38.0.zip
macOS binary (arm64)fCCAC_1.38.0.tgz
macOS binary (x86_64)fCCAC_1.38.0.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