To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("macat")
In most cases, you don't need to download the package archive at all.
macat
MicroArray Chromosome Analysis Tool
Bioconductor version: 2.14
This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'.
Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling
Maintainer: Joern Toedling <jtoedling at yahoo.de>
Citation (from within R,
enter citation("macat")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("macat")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("macat")
| R Script | MicroArray Chromosome Analysis Tool | |
| Reference Manual |
Details
| biocViews | DifferentialExpression, Microarray, Software, Visualization |
| Version | 1.38.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | Artistic-2.0 |
| Depends | Biobase, annotate |
| Imports | |
| Suggests | hgu95av2.db, stjudem |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | macat_1.38.0.tar.gz |
| Windows Binary | macat_1.38.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | macat_1.38.0.tgz |
| Mac OS X 10.9 (Mavericks) | macat_1.38.0.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
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