Integrative Multi-omics Module Network Inference with Lemon-Tree

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.
Complete list of metadatas

https://www.hal.inserm.fr/inserm-02140297
Contributor : Myriam Bodescot <>
Submitted on : Monday, May 27, 2019 - 10:57:22 AM
Last modification on : Wednesday, May 29, 2019 - 1:40:31 AM

File

journal.pcbi.1003983_PM.pdf
Publication funded by an institution

Identifiers

Citation

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

Share

Metrics

Record views

134

Files downloads

136