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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneExpressionSignature")
In most cases, you don't need to download the package archive at all.
GeneExpressionSignature
Gene Expression Signature based Similarity Metric
Bioconductor version: 3.0
This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest.
Author: Yang Cao
Maintainer: Yang Cao <yiluheihei at gmail.com>, Fei Li <pittacus at gmail.com>,Lu Han <hanl8910 at gmail.com>
Citation (from within R,
enter citation("GeneExpressionSignature")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GeneExpressionSignature")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GeneExpressionSignature")
| GeneExpressionSignature | ||
| Reference Manual |
Details
| biocViews | GeneExpression, Software |
| Version | 1.12.0 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | GPL-2 |
| Depends | R (>= 2.13), Biobase, PGSEA |
| Imports | |
| LinkingTo | |
| Suggests | apcluster, GEOquery |
| 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 | GeneExpressionSignature_1.12.0.tar.gz |
| Windows Binary | GeneExpressionSignature_1.12.0.zip |
| Mac OS X 10.6 (Snow Leopard) | GeneExpressionSignature_1.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | GeneExpressionSignature_1.12.0.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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