Ontology highlight
ABSTRACT: Motivation
Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques.Results
On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches.Availability
FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hochreiter S
PROVIDER: S-EPMC2881408 | biostudies-literature | 2010 Jun
REPOSITORIES: biostudies-literature
Hochreiter Sepp S Bodenhofer Ulrich U Heusel Martin M Mayr Andreas A Mitterecker Andreas A Kasim Adetayo A Khamiakova Tatsiana T Van Sanden Suzy S Lin Dan D Talloen Willem W Bijnens Luc L Göhlmann Hinrich W H HW Shkedy Ziv Z Clevert Djork-Arné DA
Bioinformatics (Oxford, England) 20100423 12
<h4>Motivation</h4>Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world tra ...[more]