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biocLite("clipper")
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clipper
Gene set analysis exploiting pathway topology
Bioconductor version: 3.0
clipper is a package for topological gene set analysis. It implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
Author: Paolo Martini <paolo.cavei at gmail.com>, Gabriele Sales <gabriele.sales at unipd.it>, Chiara Romualdi <chiara.romualdi at unipd.it>
Maintainer: Paolo Martini <paolo.cavei at gmail.com>
Citation (from within R,
enter citation("clipper")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("clipper")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("clipper")
| R Script | clipper | |
| Reference Manual |
Details
| biocViews | Software |
| Version | 1.6.2 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | AGPL-3 |
| Depends | R (>= 2.15.0), Matrix, graph |
| Imports | methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL |
| LinkingTo | |
| Suggests | RUnit, BiocGenerics, RCytoscape(>= 1.6.3), graphite, ALL, hgu95av2.db, MASS, BiocStyle |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | ToPASeq |
| Suggests Me | graphite |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | clipper_1.6.2.tar.gz |
| Windows Binary | clipper_1.6.2.zip |
| Mac OS X 10.6 (Snow Leopard) | clipper_1.6.2.tgz |
| Mac OS X 10.9 (Mavericks) | clipper_1.6.2.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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