  Motivation: Modelling methods that find structure in data are necessary with
the current large volumes of genomic data, and there have been various efforts
to find subsets of genes exhibiting consistent patterns over subsets of
treatments. These biclustering techniques have focused on one data source,
often gene expression data. We present a Bayesian approach for joint
biclustering of multiple data sources, extending a recent method Group Factor
Analysis (GFA) to have a biclustering interpretation with additional sparsity
assumptions. The resulting method enables data-driven detection of linear
structure present in parts of the data sources. Results: Our simulation studies
show that the proposed method reliably infers bi-clusters from heterogeneous
data sources. We tested the method on data from the NCI-DREAM drug sensitivity
prediction challenge, resulting in an excellent prediction accuracy. Moreover,
the predictions are based on several biclusters which provide insight into the
data sources, in this case on gene expression, DNA methylation, protein
abundance, exome sequence, functional connectivity fingerprints and drug
sensitivity.
