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source("http://bioconductor.org/biocLite.R")
biocLite("RNAinteract")
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RNAinteract
Estimate Pairwise Interactions from multidimensional features
Bioconductor version: 3.0
RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis.
Author: Bernd Fischer <bernd.fischer at embl.de>
Maintainer: Bernd Fischer <bernd.fischer at embl.de>
Citation (from within R,
enter citation("RNAinteract")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RNAinteract")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("RNAinteract")
| R Script | RNAinteract | |
| Reference Manual |
Details
| biocViews | CellBasedAssays, Preprocessing, QualityControl, Software, Visualization |
| Version | 1.14.0 |
| In Bioconductor since | BioC 2.8 (R-2.13) |
| License | Artistic-2.0 |
| Depends | R (>= 2.12.0), abind, locfit, Biobase |
| Imports | RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots(>= 1.13.12) |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | RNAinteractMAPK |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | RNAinteract_1.14.0.tar.gz |
| Windows Binary | RNAinteract_1.14.0.zip |
| Mac OS X 10.6 (Snow Leopard) | RNAinteract_1.14.0.tgz |
| Mac OS X 10.9 (Mavericks) | RNAinteract_1.14.0.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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