Bioconductor – GenomicOZone

DOI: 10.18129/B9.bioc.GenomicOZone  

This package is for version 3.16 of Bioconductor;
for the stable, up-to-date release version, see
GenomicOZone.

Delineate outstanding genomic zones of differential gene activity

Bioconductor version: 3.16

The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data.

Author: Hua Zhong, Mingzhou Song

Maintainer: Hua Zhong<zh9118 at gmail.com>, Mingzhou Song <joemsong at cs.nmsu.edu>

Citation (from within R,
enter citation("GenomicOZone")):

Installation

To install this package, start R (version
“4.2”) and enter:

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

BiocManager::install("GenomicOZone")

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("GenomicOZone")

 

Details

biocViews Annotation, BiomedicalInformatics, CellBiology, Clustering, CopyNumberVariation, Coverage, DifferentialExpression, DifferentialMethylation, FunctionalGenomics, FunctionalPrediction, GeneExpression, GeneRegulation, Genetics, GenomicVariation, RNASeq, Regression, Sequencing, Software, StructuralVariation, SystemsBiology, Transcription, Transcriptomics, Visualization
Version 1.12.0
In Bioconductor since BioC 3.10 (R-3.6) (3.5 years)
License LGPL (>=3)
Depends R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2
Imports grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack
LinkingTo
Suggests readxl, GEOquery, knitr, rmarkdown
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package in your R session.

Read more here: Source link