To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("prebs")
In most cases, you don't need to download the package archive at all.
prebs
Probe region expression estimation for RNA-seq data for improved microarray comparability
Bioconductor version: 3.0
The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output.
Author: Karolis Uziela and Antti Honkela
Maintainer: Karolis Uziela <karolis.uziela at scilifelab.se>
Citation (from within R,
enter citation("prebs")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("prebs")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("prebs")
| R Script | prebs User Guide | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GeneExpression, Microarray, Preprocessing, RNASeq, Sequencing, Software |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | Artistic-2.0 |
| Depends | R (>= 2.14.0), GenomicAlignments, affy |
| Imports | parallel, methods, stats, GenomicRanges(>= 1.13.3), IRanges |
| LinkingTo | |
| Suggests | prebsdata, hgu133plus2cdf, hgu133plus2probe |
| 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 | prebs_1.6.0.tar.gz |
| Windows Binary | prebs_1.6.0.zip |
| Mac OS X 10.6 (Snow Leopard) | prebs_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | prebs_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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