FABIA: factor analysis for bicluster acquisition

By Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo S Kasim, Tatsiana Khamiakova, S. Sanden, D. Lin, W. Talloen and 4 more. Bioinform., 2010

336 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/546b119d4c4a766d5d3aeafbb214354e8aca79c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Rectified Factor Networks (2015, methodology): “ures in data often break down to co-occurrences of features, i.e. features that are simultaneously present in samples. In bioinformatics sparse codes excelled in biclustering of gene expression data (Hochreiter et al., 2010) and in ﬁnding DNA sharing patterns between humans and Neanderthals (Hochrei”
A review on machine learning principles for multi-view biological data integration (2016, positive): “Simulation study revealed that sparse GFA could more accurately recover predefined biclusters in terms of F1 score compared with factor analysis for bicluster acquisition [112].”
Hierarchical cooperation of transcription factors from integration analysis of DNA sequences, ChIP-Seq and ChIA-PET data (2019, methodology): “We adopt the 51 cliques and 1223 regulated genes of K562 as an example and apply the biclustering method fabia [44] to group cliques and genes into 10 classes simultaneously (Additional file 5).”