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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("geneRecommender")
In most cases, you don't need to download the package archive at all.
geneRecommender
A gene recommender algorithm to identify genes coexpressed with a query set of genes
Bioconductor version: 3.0
This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions.
Author: Gregory J. Hather <ghather at stat.berkeley.edu>, with contributions from Art B. Owen <art at stat.stanford.edu> and Terence P. Speed <terry at stat.berkeley.edu>
Maintainer: Greg Hather <ghather at stat.berkeley.edu>
Citation (from within R,
enter citation("geneRecommender")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("geneRecommender")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("geneRecommender")
| R Script | Using the geneRecommender Package | |
| Reference Manual |
Details
| biocViews | Clustering, Microarray, Software |
| Version | 1.38.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | GPL (>= 2) |
| Depends | R (>= 1.8.0), Biobase(>= 1.4.22), methods |
| Imports | Biobase, methods, stats |
| LinkingTo | |
| Suggests | |
| 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 | geneRecommender_1.38.0.tar.gz |
| Windows Binary | geneRecommender_1.38.0.zip |
| Mac OS X 10.6 (Snow Leopard) | geneRecommender_1.38.0.tgz |
| Mac OS X 10.9 (Mavericks) | geneRecommender_1.38.0.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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