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Rmagpie
MicroArray Gene-expression-based Program In Error rate estimation
Bioconductor version: 3.0
Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.
Author: Camille Maumet <Rmagpie at gmail.com>, with contributions from C. Ambroise J. Zhu
Maintainer: Camille Maumet <Rmagpie at gmail.com>
Citation (from within R,
enter citation("Rmagpie")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Rmagpie")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Rmagpie")
| R Script | Rmagpie Examples | |
| Reference Manual |
Details
| biocViews | Classification, Microarray, Software |
| Version | 1.22.0 |
| In Bioconductor since | BioC 2.4 (R-2.9) |
| License | GPL (>= 3) |
| Depends | R (>= 2.6.1), Biobase(>= 2.5.5) |
| Imports | Biobase(>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils |
| LinkingTo | |
| Suggests | xtable |
| SystemRequirements | |
| Enhances | |
| URL | http://www.bioconductor.org/ |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | Rmagpie_1.22.0.tar.gz |
| Windows Binary | Rmagpie_1.22.0.zip |
| Mac OS X 10.6 (Snow Leopard) | Rmagpie_1.22.0.tgz |
| Mac OS X 10.9 (Mavericks) | Rmagpie_1.22.0.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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