To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ABSSeq")
In most cases, you don't need to download the package archive at all.
ABSSeq
ABSSeq: a new RNA-Seq analysis method based on absolute expression differences and generalized Poisson model
Bioconductor version: 3.0
Inferring differential expression genes by absolute expression differences between two groups, utilizing generalized Poisson model to account for over-dispersion across samples and heterogeneity of differential expression across genes.
Author: Wentao Yang
Maintainer: Wentao Yang <wyang at zoologie.uni-kiel.de>
Citation (from within R,
enter citation("ABSSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ABSSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ABSSeq")
| R Script | ABSSeq | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DifferentialExpression, Software |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | GPL (>= 3) |
| Depends | R (>= 2.10), methods |
| Imports | Rcpp |
| LinkingTo | Rcpp |
| Suggests | |
| 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 | ABSSeq_1.2.0.tar.gz |
| Windows Binary | ABSSeq_1.2.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | ABSSeq_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | ABSSeq_1.2.0.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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