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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SeqGSEA")
In most cases, you don't need to download the package archive at all.
SeqGSEA
Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing
Bioconductor version: 2.14
The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.
Author: Xi Wang <Xi.Wang at newcastle.edu.au>
Maintainer: Xi Wang <Xi.Wang at mdc-berlin.de>
Citation (from within R,
enter citation("SeqGSEA")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SeqGSEA")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SeqGSEA")
| R Script | Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DifferentialExpression, GeneExpression, GeneSetEnrichment, RNASeq, Sequencing, Software |
| Version | 1.4.2 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>= 3) |
| Depends | Biobase, doParallel, DESeq |
| Imports | methods, biomaRt |
| Suggests | easyRNASeq, GenomicRanges |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | SeqGSEA_1.4.2.tar.gz |
| Windows Binary | SeqGSEA_1.4.2.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | SeqGSEA_1.4.2.tgz |
| Mac OS X 10.9 (Mavericks) | SeqGSEA_1.4.2.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
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