To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("les")
In most cases, you don't need to download the package archive at all.
les
Identifying Differential Effects in Tiling Microarray Data
Bioconductor version: 2.14
The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes.
Author: Julian Gehring, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <julian.gehring at embl.de>
Citation (from within R,
enter citation("les")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("les")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("les")
| R Script | Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework | |
| Reference Manual | ||
| Text | NEWS | |
| Text | LICENSE |
Details
| biocViews | ChIPchip, DNAMethylation, DifferentialExpression, Microarray, Software, Transcription |
| Version | 1.14.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | GPL-3 |
| Depends | R (>= 2.13.2), methods, graphics, fdrtool |
| Imports | boot, gplots, RColorBrewer |
| Suggests | Biobase, limma |
| System Requirements | |
| URL | http://julian-gehring.github.com/les/ |
| Depends On Me | |
| Imports Me | GSRI |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | les_1.14.0.tar.gz |
| Windows Binary | les_1.14.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | les_1.14.0.tgz |
| Mac OS X 10.9 (Mavericks) | les_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
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