To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PICS")
In most cases, you don't need to download the package archive at all.
PICS
Probabilistic inference of ChIP-seq
Bioconductor version: 3.0
Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach.
Author: Xuekui Zhang <xzhang at stat.ubc.ca>, Raphael Gottardo <rgottard at fhcrc.org>
Maintainer: Renan Sauteraud <rsautera at fhcrc.org>
Citation (from within R,
enter citation("PICS")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PICS")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("PICS")
| R Script | The PICS users guide | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | ChIPSeq, Clustering, Sequencing, Software, Visualization |
| Version | 2.10.0 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | Artistic-2.0 |
| Depends | R (>= 2.14.0), BiocGenerics(>= 0.1.3) |
| Imports | methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments |
| LinkingTo | |
| Suggests | ShortRead, rtracklayer, parallel |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | PING |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | PICS_2.10.0.tar.gz |
| Windows Binary | PICS_2.10.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | PICS_2.10.0.tgz |
| Mac OS X 10.9 (Mavericks) | PICS_2.10.0.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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