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biocLite("ArrayTools")
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ArrayTools
geneChip Analysis Package
Bioconductor version: 3.0
This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases.
Author: Xiwei Wu, Arthur Li
Maintainer: Arthur Li <xueli at coh.org>
Citation (from within R,
enter citation("ArrayTools")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ArrayTools")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ArrayTools")
| R Script | 1. Primer | |
| Reference Manual |
Details
| biocViews | Annotation, DifferentialExpression, Microarray, OneChannel, Preprocessing, QualityControl, ReportWriting, Software, StatisticalMethod, Visualization |
| Version | 1.26.0 |
| In Bioconductor since | BioC 2.3 (R-2.8) |
| License | LGPL (>= 2.0) |
| Depends | R (>= 2.7.0), affy(>= 1.23.4), Biobase(>= 2.5.5), methods |
| Imports | affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable |
| LinkingTo | |
| Suggests | simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db |
| 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 | ArrayTools_1.26.0.tar.gz |
| Windows Binary | ArrayTools_1.26.0.zip |
| Mac OS X 10.6 (Snow Leopard) | ArrayTools_1.26.0.tgz |
| Mac OS X 10.9 (Mavericks) | ArrayTools_1.26.0.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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