FABIA: factor analysis for bicluster acquisition.
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.
Hochreiter
Sepp
S
Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at
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
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