To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("FEM")
In most cases, you don't need to download the package archive at all.
FEM
Identification of FunctionalEpigenetic Modules
Bioconductor version: 3.0
FEM can dentify interactome hotspots of differential promoter methylation and differential ex-pression, where an inverse association between promoter methylation and gene expression is assumed.
Author: Andrew E. Teschendorff and Yinming Jiao
Maintainer: Andrew E. Teschendorff <andrew at picb.ac.cn>, Yinming Jiao <20907099 at mail.zju.edu.cn>
Citation (from within R,
enter citation("FEM")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("FEM")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("FEM")
| R Script | A R package to identify interactome hotspots of differential promoter methylation and differential expression, where an inverse association between promoter methylation and gene expression is assumed1. | |
| Reference Manual |
Details
| biocViews | DNAMethylation, DifferentialExpression, DifferentialMethylation, GeneRegulation, Network, NetworkEnrichment, Software, SystemsBiology |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL (>=2) |
| Depends | R (>= 2.10), Matrix, igraph, marray, corrplot, impute, limma, org.Hs.eg.db, graph, BiocGenerics |
| Imports | |
| LinkingTo | |
| 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 | FEM_1.0.0.tar.gz |
| Windows Binary | FEM_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | FEM_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | FEM_1.0.0.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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