  Module network inference is an established statistical method to reconstruct
co-expression modules and their upstream regulatory programs from integrated
multi-omics datasets measuring the activity levels of various cellular
components across different individuals, experimental conditions or time points
of a dynamic process. We have developed Lemon-Tree, an open-source,
platform-independent, modular, extensible software package implementing
state-of-the-art ensemble methods for module network inference. We benchmarked
Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree
algorithms compare favorably with state-of-the-art module network inference
software. We also analyzed a large dataset of somatic copy-number alterations
and gene expression levels measured in glioblastoma samples from The Cancer
Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma
oncogenes and tumor suppressors as master regulators in the inferred module
network. Novel candidate driver genes predicted by Lemon-Tree were validated
using tumor pathway and survival analyses. Lemon-Tree is available from
http://lemon-tree.googlecode.com under the GNU General Public License version
2.0.
