To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("iBBiG")
In most cases, you don't need to download the package archive at all.
iBBiG
Iterative Binary Biclustering of Genesets
Bioconductor version: 3.0
iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes
Author: Daniel Gusenleitner, Aedin Culhane
Maintainer: Aedin Culhane <aedin at jimmy.harvard.edu>
Citation (from within R,
enter citation("iBBiG")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("iBBiG")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("iBBiG")
| R Script | iBBiG User Manual | |
| Reference Manual |
Details
| biocViews | Annotation, Clustering, GeneSetEnrichment, Software |
| Version | 1.10.0 |
| In Bioconductor since | BioC 2.10 (R-2.15) |
| License | Artistic-2.0 |
| Depends | biclust |
| Imports | stats4, xtable, ade4 |
| LinkingTo | |
| Suggests | methods |
| SystemRequirements | |
| Enhances | |
| URL | http://bcb.dfci.harvard.edu/~aedin/publications/ |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | iBBiG_1.10.0.tar.gz |
| Windows Binary | iBBiG_1.10.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | iBBiG_1.10.0.tgz |
| Mac OS X 10.9 (Mavericks) | iBBiG_1.10.0.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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