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fabia
FABIA: Factor Analysis for Bicluster Acquisition
Bioconductor version: 3.0
Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C.
Author: Sepp Hochreiter <hochreit at bioinf.jku.at>
Maintainer: Sepp Hochreiter <hochreit at bioinf.jku.at>
Citation (from within R,
enter citation("fabia")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("fabia")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("fabia")
| R Script | FABIA: Manual for the R package | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Clustering, DifferentialExpression, Microarray, MultipleComparison, Software, StatisticalMethod, Visualization |
| Version | 2.12.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | LGPL (>= 2.1) |
| Depends | R (>= 2.8.0), Biobase |
| Imports | methods, graphics, grDevices, stats, utils |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | http://www.bioinf.jku.at/software/fabia/fabia.html |
| Depends On Me | hapFabia |
| Imports Me | |
| Suggests Me | fabiaData |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | fabia_2.12.0.tar.gz |
| Windows Binary | fabia_2.12.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | fabia_2.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | fabia_2.12.0.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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