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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RankProd")
In most cases, you don't need to download the package archive at all.
RankProd
Rank Product method for identifying differentially expressed genes with application in meta-analysis
Bioconductor version: 2.14
Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification.
Author: Fangxin Hong <fxhong at jimmy.harvard.edu> and Ben Wittner <Wittner.Ben at mgh.harvard.edu> with contribution from Rainer Breitling <r.breitling at bio.gla.ac.uk>, Colin Smith <colin at colinsmith.org>, and Florian Battke <battke at informatik.uni-tuebingen.de>
Maintainer: Fangxin Hong <fxhong at jimmy.harvard.edu>
Citation (from within R,
enter citation("RankProd")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RankProd")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("RankProd")
| R Script | RankProd Tutorial | |
| Reference Manual | ||
| Text | LICENSE |
Details
| biocViews | DifferentialExpression, Software |
| Version | 2.36.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | file LICENSE |
| Depends | R (>= 1.9.0) |
| Imports | graphics |
| Suggests | |
| System Requirements | |
| URL | |
| Depends On Me | RNAither, tRanslatome |
| Imports Me | HTSanalyzeR |
| Suggests Me | oneChannelGUI |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | RankProd_2.36.0.tar.gz |
| Windows Binary | RankProd_2.36.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | RankProd_2.36.0.tgz |
| Mac OS X 10.9 (Mavericks) | RankProd_2.36.0.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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