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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("EasyqpcR")
In most cases, you don't need to download the package archive at all.
EasyqpcR
EasyqpcR for low-throughput real-time quantitative PCR data analysis
Bioconductor version: 2.14
This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis.
Author: Le Pape Sylvain
Maintainer: Le Pape Sylvain <sylvain.le.pape at univ-poitiers.fr>
Citation (from within R,
enter citation("EasyqpcR")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("EasyqpcR")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("EasyqpcR")
| R Script | EasyqpcR | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneExpression, Software, qPCR |
| Version | 1.7.0 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL (>=2) |
| Depends | |
| Imports | plyr, matrixStats, plotrix, gWidgetsRGtk2 |
| Suggests | SLqPCR, qpcrNorm, qpcR, knitr |
| 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 | EasyqpcR_1.7.0.tar.gz |
| Windows Binary | EasyqpcR_1.7.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | EasyqpcR_1.7.0.tgz |
| Mac OS X 10.9 (Mavericks) | EasyqpcR_1.7.0.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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