To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Metab")
In most cases, you don't need to download the package archive at all.
Metab
Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS.
Bioconductor version: 3.0
Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities.
Author: Raphael Aggio <ragg005 at aucklanduni.ac.nz>
Maintainer: Raphael Aggio <ragg005 at aucklanduni.ac.nz>
Citation (from within R,
enter citation("Metab")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Metab")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Metab")
| R Script | Applying Metab | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | AMDIS, GCMS, MassSpectrometry, Metabolomics, Software |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL (>=2) |
| Depends | xcms, R (>= 3.0.1), svDialogs |
| Imports | pander |
| LinkingTo | |
| Suggests | RUnit, BiocGenerics |
| 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 | Metab_1.0.0.tar.gz |
| Windows Binary | Metab_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | Metab_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | Metab_1.0.0.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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