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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneSelectMMD")
In most cases, you don't need to download the package archive at all.
GeneSelectMMD
Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions
Bioconductor version: 3.0
Gene selection based on a mixture of marginal distributions
Author: Jarrett Morrow <remdj at channing.harvard.edu>, Weiliang Qiu <stwxq at channing.harvard.edu>, Wenqing He <whe at stats.uwo.ca>, Xiaogang Wang <stevenw at mathstat.yorku.ca>, Ross Lazarus <ross.lazarus at channing.harvard.edu>.
Maintainer: Weiliang Qiu <stwxq at channing.harvard.edu>
Citation (from within R,
enter citation("GeneSelectMMD")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneSelectMMD")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GeneSelectMMD")
| R Script | Gene Selection based on a mixture of marginal distributions | |
| Reference Manual |
Details
| biocViews | DifferentialExpression, Software |
| Version | 2.10.0 |
| In Bioconductor since | BioC 2.4 (R-2.9) |
| License | GPL (>= 2) |
| Depends | R (>= 2.13.2), Biobase |
| Imports | Biobase, MASS, graphics, stats, survival, limma |
| LinkingTo | |
| Suggests | ALL |
| 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 | GeneSelectMMD_2.10.0.tar.gz |
| Windows Binary | GeneSelectMMD_2.10.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | GeneSelectMMD_2.10.0.tgz |
| Mac OS X 10.9 (Mavericks) | GeneSelectMMD_2.10.0.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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