title: FABIA: factor analysis for bicluster acquisition.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at
authors.forename: Sepp
authors.initials: S
authors.lastname: Hochreiter
authors.forename: Ulrich
authors.initials: U
authors.lastname: Bodenhofer
authors.forename: Martin
authors.initials: M
authors.lastname: Heusel
authors.forename: Andreas
authors.initials: A
authors.lastname: Mayr
authors.forename: Andreas
authors.initials: A
authors.lastname: Mitterecker
authors.forename: Adetayo
authors.initials: A
authors.lastname: Kasim
authors.forename: Tatsiana
authors.initials: T
authors.lastname: Khamiakova
authors.forename: Suzy
authors.initials: S
authors.lastname: Van Sanden
authors.forename: Dan
authors.initials: D
authors.lastname: Lin
authors.forename: Willem
authors.initials: W
authors.lastname: Talloen
authors.forename: Luc
authors.initials: L
authors.lastname: Bijnens
authors.forename: Hinrich W H
authors.initials: HW
authors.lastname: Göhlmann
authors.forename: Ziv
authors.initials: Z
authors.lastname: Shkedy
authors.forename: Djork-Arné
authors.initials: DA
authors.lastname: Clevert
mesh_terms: D000465:Algorithms; D005163:Factor Analysis, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D012984:Software