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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("quantro")
In most cases, you don't need to download the package archive at all.
quantro
A test for when to use quantile normalization
Bioconductor version: 3.0
A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.
Author: Stephanie Hicks and Rafael Irizarry
Maintainer: Stephanie Hicks <shicks at jimmy.harvard.edu>
Citation (from within R,
enter citation("quantro")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("quantro")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("quantro")
| R Script | The quantro user's guide | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Microarray, MultipleComparison, Normalization, Preprocessing, Sequencing, Software |
| Version | 1.0.0 |
| In Bioconductor since | BioC 3.0 (R-3.1) |
| License | GPL (>=3) |
| Depends | R (>= 3.1.1) |
| Imports | Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods |
| LinkingTo | |
| Suggests | knitr, RUnit, BiocGenerics, BiocStyle |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | quantro_1.0.0.tar.gz |
| Windows Binary | quantro_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | quantro_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | quantro_1.0.0.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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