To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SPIA")
In most cases, you don't need to download the package archive at all.
SPIA
Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations
Bioconductor version: 3.0
This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study.
Author: Adi Laurentiu Tarca <atarca at med.wayne.edu>, Purvesh Kathri <purvesh at cs.wayne.edu> and Sorin Draghici <sorin at wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca at med.wayne.edu>
Citation (from within R,
enter citation("SPIA")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SPIA")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SPIA")
| R Script | SPIA | |
| Reference Manual |
Details
| biocViews | GraphAndNetwork, Microarray, Software |
| Version | 2.18.0 |
| In Bioconductor since | BioC 2.4 (R-2.9) |
| License | GPL (>= 2) |
| Depends | R (>= 2.14.0), graphics, KEGGgraph |
| Imports | graphics |
| LinkingTo | |
| Suggests | graph, Rgraphviz, hgu133plus2.db |
| SystemRequirements | |
| Enhances | |
| URL | http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 |
| Depends On Me | |
| Imports Me | EnrichmentBrowser, ToPASeq |
| Suggests Me | graphite, KEGGgraph |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | SPIA_2.18.0.tar.gz |
| Windows Binary | SPIA_2.18.0.zip |
| Mac OS X 10.6 (Snow Leopard) | SPIA_2.18.0.tgz |
| Mac OS X 10.9 (Mavericks) | SPIA_2.18.0.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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