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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10974

Title: FABIA: factor analysis for bicluster acquisition
Authors: Hochreiter, Sepp
Bodenhofer, Ulrich
Heusel, Martin
Mayr, Andreas
Mitterecker, Andreas
KASIM, Adetayo
LIN, Dan
Talloen, Willem
Gohlmann, Hinrich W. H.
Clevert, Djork-Arne
Issue Date: 2010
Citation: BIOINFORMATICS, 26 (12). p. 1520-1527
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.
Notes: [Hochreiter, Sepp; Bodenhofer, Ulrich; Heusel, Martin; Mayr, Andreas; Mitterecker, Andreas; Clevert, Djork-Arne] Johannes Kepler Univ Linz, Inst Bioinformat, A-4040 Linz, Austria. [Kasim, Adetayo; Khamiakova, Tatsiana; Van Sanden, Suzy; Lin, Dan; Shkedy, Ziv] Hasselt Univ, Inst Biostat & Stat Bioinformat, Hasselt, Belgium. [Talloen, Willem; Bijnens, Luc; Gohlmann, Hinrich W. H.] Johnson & Johnson Pharmaceut Res & Dev, Div Janssen Pharmaceut, Beerse, Belgium. [Clevert, Djork-Arne] Charite, Dept Nephrol & Internal Intens Care, Berlin, Germany. hochreit@bioinf.jku.at
URI: http://hdl.handle.net/1942/10974
DOI: 10.1093/bioinformatics/btq227
ISI #: 000278689000058
ISSN: 1367-4803
Category: A1
Type: Journal Contribution
Validation: ecoom, 2011
Appears in Collections: Research publications

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