To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DOSE")
In most cases, you don't need to download the package archive at all.
DOSE
Disease Ontology Semantic and Enrichment analysis
Bioconductor version: 3.0
This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.
Author: Guangchuang Yu, Li-Gen Wang
Maintainer: Guangchuang Yu <guangchuangyu at gmail.com>
Citation (from within R,
enter citation("DOSE")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("DOSE")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DOSE")
| R Script | DOSE - an R package for Disease Ontology Semantic and Enrichment | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Annotation, GeneSetEnrichment, MultipleComparison, Software, Visualization |
| Version | 2.4.0 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | Artistic-2.0 |
| Depends | R (>= 3.0.0) |
| Imports | methods, plyr, qvalue, stats4, AnnotationDbi, DO.db, igraph, scales, reshape2, graphics, GOSemSim, grid, ggplot2 |
| LinkingTo | |
| Suggests | org.Hs.eg.db, clusterProfiler, ReactomePA, ChIPseeker, knitr |
| SystemRequirements | |
| Enhances | |
| URL | https://github.com/GuangchuangYu/DOSE |
| Depends On Me | |
| Imports Me | clusterProfiler, facopy, ReactomePA |
| Suggests Me | ChIPseeker, GOSemSim |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | DOSE_2.4.0.tar.gz |
| Windows Binary | DOSE_2.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | DOSE_2.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | DOSE_2.4.0.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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