baySeq
Empirical Bayesian analysis of patterns of differential expression in count data
Bioconductor version: 2.6
This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.
Author: Thomas J. Hardcastle
Maintainer: Thomas J. Hardcastle <tjh48 at cam.ac.uk>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("baySeq")
To cite this package in a publication, start R and enter:
citation("baySeq")
Documentation
| R Script | baySeq | |
| Reference Manual |
Details
| biocViews | Bioinformatics, HighThroughputSequencing, DifferentialExpression, MultipleComparisons, SAGE |
| Depends | R (>= 2.3.0), methods |
| Imports | |
| Suggests | snow |
| System Requirements | |
| License | GPL-3 |
| URL | |
| Depends On Me | segmentSeq |
| Imports Me | segmentSeq |
| Suggests Me | |
| Version | 1.2.0 |
| Since | Bioconductor 2.5 (R-2.10) |
Package Downloads
| Package Source | baySeq_1.2.0.tar.gz |
| Windows Binary | baySeq_1.2.0.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | baySeq_1.2.0.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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