Integrative Multi-omics Module Network Inference with Lemon-Tree - Archive ouverte HAL Access content directly
Journal Articles PLoS Computational Biology Year : 2015

Integrative Multi-omics Module Network Inference with Lemon-Tree

(1) , (1) , (2)
1
2

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.
Fichier principal
Vignette du fichier
journal.pcbi.1003983_PM.pdf (123.08 Ko) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

inserm-02140297 , version 1 (27-05-2019)

Identifiers

Cite

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⟩
97 View
50 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More