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 Bijnens, Hinrich W.H. Göhlmann, Ziv Shkedy, Djork Arné Clevert

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

180 Zitate (Scopus)

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. Contact: hochreit@bioinf.jku.at. Supplementary information: Supplementary data are available at Bioinformatics online.

OriginalspracheEnglisch
Aufsatznummerbtq227
Seiten (von - bis)1520-1527
Seitenumfang8
FachzeitschriftBioinformatics
Jahrgang26
Ausgabenummer12
DOIs
PublikationsstatusVeröffentlicht - 23 Apr 2010

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