To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("plrs")
In most cases, you don't need to download the package archive at all.
plrs
Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression
Bioconductor version: 3.0
The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS).
Author: Gwenael G.R. Leday
Maintainer: Gwenael G.R. Leday to <gleday at few.vu.nl>
Citation (from within R,
enter citation("plrs")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("plrs")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("plrs")
| R Script | plrs | |
| Reference Manual |
Details
| biocViews | Regression, Software |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>=2.0) |
| Depends | R (>= 2.10), Biobase |
| Imports | BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils |
| LinkingTo | |
| Suggests | mvtnorm, methods |
| 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 | plrs_1.6.0.tar.gz |
| Windows Binary | plrs_1.6.0.zip |
| Mac OS X 10.6 (Snow Leopard) | plrs_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | plrs_1.6.0.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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