To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("rama")
In most cases, you don't need to download the package archive at all.
rama
Robust Analysis of MicroArrays
Bioconductor version: 3.0
Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance.
Author: Raphael Gottardo
Maintainer: Raphael Gottardo <raph at stat.ubc.ca>
Citation (from within R,
enter citation("rama")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("rama")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("rama")
| R Script | rama Tutorial | |
| Reference Manual |
Details
| biocViews | Microarray, Preprocessing, QualityControl, Software, TwoChannel |
| Version | 1.40.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | GPL (>= 2) |
| Depends | R (>= 2.5.0) |
| Imports | |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | bridge |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | rama_1.40.0.tar.gz |
| Windows Binary | rama_1.40.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | rama_1.40.0.tgz |
| Mac OS X 10.9 (Mavericks) | rama_1.40.0.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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