To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("tspair")
In most cases, you don't need to download the package archive at all.
tspair
Top Scoring Pairs for Microarray Classification
Bioconductor version: 3.0
These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers.
Author: Jeffrey T. Leek <jtleek at jhu.edu>
Maintainer: Jeffrey T. Leek <jtleek at jhu.edu>
Citation (from within R,
enter citation("tspair")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("tspair")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("tspair")
| R Script | tspTutorial | |
| Reference Manual |
Details
| biocViews | Microarray, Software |
| Version | 1.24.0 |
| In Bioconductor since | BioC 2.4 (R-2.9) |
| License | GPL-2 |
| Depends | R (>= 2.10), Biobase(>= 2.4.0) |
| Imports | |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | stepwiseCM |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | tspair_1.24.0.tar.gz |
| Windows Binary | tspair_1.24.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | tspair_1.24.0.tgz |
| Mac OS X 10.9 (Mavericks) | tspair_1.24.0.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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