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- ExpressionView
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ExpressionView")
In most cases, you don't need to download the package archive at all.
ExpressionView
Visualize biclusters identified in gene expression data
Bioconductor version: 2.14
ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser.
Author: Andreas Luscher <andreas.luescher at a3.epfl.ch>
Maintainer: Gabor Csardi <csardi.gabor at gmail.com>
Citation (from within R,
enter citation("ExpressionView")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ExpressionView")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ExpressionView")
| R Script | ExpressionView | |
| R Script | ExpressionView file format | |
| R Script | How the ordering algorithm works | |
| Reference Manual |
Details
| biocViews | Classification, GeneExpression, Microarray, Software, Visualization |
| Version | 1.16.1 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | GPL (>= 2) |
| Depends | caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi |
| Imports | methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi |
| Suggests | ALL, hgu95av2.db, biclust, affy |
| 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 | ExpressionView_1.16.1.tar.gz |
| Windows Binary | ExpressionView_1.16.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | ExpressionView_1.16.1.tgz |
| Mac OS X 10.9 (Mavericks) | ExpressionView_1.16.1.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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