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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("spikeLI")
In most cases, you don't need to download the package archive at all.
spikeLI
Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool
Bioconductor version: 2.14
SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006).
Author: Delphine Baillon, Paul Leclercq <paulleclercq at hotmail.com>, Sarah Ternisien, Thomas Heim, Enrico Carlon <enrico.carlon at fys.kuleuven.be>
Maintainer: Enrico Carlon <enrico.carlon at fys.kuleuven.be>
Citation (from within R,
enter citation("spikeLI")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("spikeLI")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("spikeLI")
| R Script | spikeLI | |
| Reference Manual |
Details
| biocViews | Microarray, QualityControl, Software |
| Version | 2.24.0 |
| In Bioconductor since | BioC 1.9 (R-2.4) |
| License | GPL-2 |
| Depends | |
| Imports | graphics, grDevices, stats, utils |
| Suggests | |
| 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 | spikeLI_2.24.0.tar.gz |
| Windows Binary | spikeLI_2.24.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | spikeLI_2.24.0.tgz |
| Mac OS X 10.9 (Mavericks) | spikeLI_2.24.0.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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