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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ABarray")
In most cases, you don't need to download the package archive at all.
ABarray
Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data.
Bioconductor version: 3.0
Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used.
Author: Yongming Andrew Sun
Maintainer: Yongming Andrew Sun <sunya at appliedbiosystems.com>
Citation (from within R,
enter citation("ABarray")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ABarray")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ABarray")
| R Script | ABarray gene expression | |
| R Script | ABarray gene expression GUI interface | |
| Reference Manual |
Details
| biocViews | Microarray, OneChannel, Preprocessing, Software |
| Version | 1.34.0 |
| In Bioconductor since | BioC 1.9 (R-2.4) |
| License | GPL |
| Depends | |
| Imports | Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils |
| LinkingTo | |
| Suggests | limma, LPE |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | ABarray_1.34.0.tar.gz |
| Windows Binary | ABarray_1.34.0.zip |
| Mac OS X 10.6 (Snow Leopard) | ABarray_1.34.0.tgz |
| Mac OS X 10.9 (Mavericks) | ABarray_1.34.0.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
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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