gaga
GaGa hierarchical model for high-throughput data analysis
Bioconductor version: 2.10
Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).
Author: David Rossell <rosselldavid at gmail.com>.
Maintainer: David Rossell <rosselldavid at gmail.com>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("gaga")
To cite this package in a publication, start R and enter:
citation("gaga")
Documentation
| R Script | Manual for the gaga library | |
| Reference Manual |
Details
| biocViews | OneChannel,MassSpectrometry,MultipleComparisons,DifferentialExpression,Classification, Software |
| Depends | R (>= 2.8.0), Biobase, coda, EBarrays, mgcv |
| Imports | |
| Suggests | |
| System Requirements | |
| License | GPL (>= 2) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Version | 2.2.0 |
| Since | Bioconductor 2.2 (R-2.7) |
Package Downloads
| Package Source | gaga_2.2.0.tar.gz |
| Windows Binary | gaga_2.2.0.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | gaga_2.2.0.tgz |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Variants
- Flow Cytometry and other assays
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