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Journal Articles PLoS Computational Biology Year : 2015

Integrative Multi-omics Module Network Inference with Lemon-Tree

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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 under the GNU General Public License version 2.0.
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inserm-02140297 , version 1 (27-05-2019)



Eric Bonnet, Laurence Calzone, Tom Michoel. Integrative Multi-omics Module Network Inference with Lemon-Tree. PLoS Computational Biology, 2015, 11 (2), pp.e1003983. ⟨10.1371/journal.pcbi.1003983⟩. ⟨inserm-02140297⟩
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