To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CSAR")
In most cases, you don't need to download the package archive at all.
CSAR
Statistical tools for the analysis of ChIP-seq data
Bioconductor version: 3.0
Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation.
Author: Jose M Muino
Maintainer: Jose M Muino <jose.muino at wur.nl>
Citation (from within R,
enter citation("CSAR")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CSAR")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CSAR")
| R Script | CSAR Vignette | |
| Reference Manual |
Details
| biocViews | ChIPSeq, Genetics, Software, Transcription |
| Version | 1.18.0 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | Artistic-2.0 |
| Depends | R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges |
| Imports | stats, utils |
| LinkingTo | |
| Suggests | ShortRead, Biostrings |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | NarrowPeaks |
| Suggests Me | NarrowPeaks |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | CSAR_1.18.0.tar.gz |
| Windows Binary | CSAR_1.18.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | CSAR_1.18.0.tgz |
| Mac OS X 10.9 (Mavericks) | CSAR_1.18.0.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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