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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("biovizBase")
In most cases, you don't need to download the package archive at all.
biovizBase
Basic graphic utilities for visualization of genomic data.
Bioconductor version: 2.14
The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency.
Author: Tengfei Yin, Michael Lawrence, Dianne Cook
Maintainer: Tengfei Yin <yintengfei at gmail.com>
Citation (from within R,
enter citation("biovizBase")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("biovizBase")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("biovizBase")
| R Script | An Introduction to biovizBase | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Infrastructure, Preprocessing, Software, Visualization |
| Version | 1.12.3 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | Artistic-2.0 |
| Depends | R (>= 2.10), methods |
| Imports | methods, grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, IRanges, GenomicRanges(>= 1.13.3), Biostrings, Rsamtools(>= 1.13.1), GenomicAlignments, GenomicFeatures, AnnotationDbi, VariantAnnotation |
| Suggests | BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer |
| System Requirements | |
| URL | |
| Depends On Me | CAFE, qrqc |
| Imports Me | ggbio, Gviz, qrqc |
| Suggests Me | Rariant |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | biovizBase_1.12.3.tar.gz |
| Windows Binary | biovizBase_1.12.3.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | biovizBase_1.12.3.tgz |
| Mac OS X 10.9 (Mavericks) | biovizBase_1.12.3.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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