Integrative Multi-omics Module Network Inference with Lemon-Tree. Bonnet, E., Calzone, L., & Michoel, T. PLoS Computational Biology, 11(2):e1003983, February, 2015. ![Integrative Multi-omics Module Network Inference with Lemon-Tree [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{bonnet_integrative_2015,
title = {Integrative {Multi}-omics {Module} {Network} {Inference} with {Lemon}-{Tree}},
volume = {11},
issn = {1553-7358},
url = {http://dx.plos.org/10.1371/journal.pcbi.1003983},
doi = {10.1371/journal.pcbi.1003983},
abstract = {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.},
language = {English},
number = {2},
journal = {PLoS Computational Biology},
author = {Bonnet, Eric and Calzone, Laurence and Michoel, Tom},
month = feb,
year = {2015},
pages = {e1003983},
}
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