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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GOSemSim")
In most cases, you don't need to download the package archive at all.
GOSemSim
GO-terms Semantic Similarity Measures
Bioconductor version: 3.0
Implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for estimating GO semantic similarities. Support many species, including Anopheles, Arabidopsis, Bovine, Canine, Chicken, Chimp, Coelicolor, E coli strain K12 and Sakai, Fly, Gondii, Human, Malaria, Mouse, Pig, Rhesus, Rat, Worm, Xenopus, Yeast, and Zebrafish.
Author: Guangchuang Yu
Maintainer: Guangchuang Yu <guangchuangyu at gmail.com>
Citation (from within R,
enter citation("GOSemSim")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GOSemSim")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GOSemSim")
| R Script | An introduction to GOSemSim | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Clustering, GO, Network, Pathways, Software |
| Version | 1.24.1 |
| In Bioconductor since | BioC 2.4 (R-2.9) |
| License | Artistic-2.0 |
| Depends | R (>= 3.0.0) |
| Imports | Rcpp, AnnotationDbi, GO.db |
| LinkingTo | Rcpp |
| Suggests | DOSE, clusterProfiler, org.Hs.eg.db, ChIPseeker, ReactomePA, BiocInstaller, knitr |
| SystemRequirements | |
| Enhances | |
| URL | https://github.com/GuangchuangYu/GOSemSim |
| Depends On Me | tRanslatome |
| Imports Me | clusterProfiler, DOSE, Rcpi |
| Suggests Me | ChIPseeker, ReactomePA, SemDist |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GOSemSim_1.24.1.tar.gz |
| Windows Binary | GOSemSim_1.24.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | GOSemSim_1.24.1.tgz |
| Mac OS X 10.9 (Mavericks) | GOSemSim_1.24.1.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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