FABIA: Factor Analysis for Bicluster Acquisition
Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, Suzy Van Sanden, Dan Lin, Willem Talloen, Luc Bijens, Heinrich H.W. Göhlmann, Ziv Shkedy, Djork-ArnéClevertBioinformatics Advance Access published on April 23, 2010, doi:10.1093/bioinformatics/btq227 (online) 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 data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these data sets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray data sets with known sub-clusters, 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 data sets, results, and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html. Contact:
hochreit@bioinf.jku.at