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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneAnswers")
In most cases, you don't need to download the package archive at all.
GeneAnswers
Integrated Interpretation of Genes
Bioconductor version: 3.0
GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test.
Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin
Maintainer: Lei Huang <lhuang7 at uchicago.edu> and Gang Feng <gilbertfeng at gmail.edu>
Citation (from within R,
enter citation("GeneAnswers")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneAnswers")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GeneAnswers")
| R Script | GeneAnswers | |
| R Script | GeneAnswers web-based visualization module | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DataRepresentation, GraphsAndNetworks, Infrastructure, Software, Visualization |
| Version | 2.8.0 |
| In Bioconductor since | BioC 2.5 (R-2.10) |
| License | LGPL (>= 2) |
| Depends | R (>= 3.0.0), igraph, RCurl, annotate, Biobase(>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer |
| Imports | RBGL, annotate, downloader |
| LinkingTo | |
| Suggests | GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph |
| 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 | GeneAnswers_2.8.0.tar.gz |
| Windows Binary | GeneAnswers_2.8.0.zip |
| Mac OS X 10.6 (Snow Leopard) | GeneAnswers_2.8.0.tgz |
| Mac OS X 10.9 (Mavericks) | GeneAnswers_2.8.0.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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