- Home
- Bioconductor 2.14
- Software Packages
- attract
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("attract")
In most cases, you don't need to download the package archive at all.
attract
Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape
Bioconductor version: 2.14
This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression.
Author: Jessica Mar
Maintainer: Jessica Mar <jess at jimmy.harvard.edu>
Citation (from within R,
enter citation("attract")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("attract")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("attract")
| R Script | Package | |
| Reference Manual |
Details
| biocViews | GeneExpression, Software, StatisticalMethod |
| Version | 1.16.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | LGPL (>= 2.0) |
| Depends | R (>= 2.10.0), AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats |
| Imports | Biobase, AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats |
| Suggests | illuminaHumanv1.db |
| 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 | attract_1.16.0.tar.gz |
| Windows Binary | attract_1.16.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | attract_1.16.0.tgz |
| Mac OS X 10.9 (Mavericks) | attract_1.16.0.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
Post questions about Bioconductor packages to our mailing lists. Read the posting guide before posting!
Contact us: support.bioconductor.org
Hosting provided by Fred Hutchinson Cancer Research Center
Copyright © 2003 - 2014
